{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inżynieria uczenia maszynowego\n",
    "### 13 marca 2024\n",
    "# 2. Dane"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Plan na dzisiaj\n",
    "1. Motywacja\n",
    "2. Podział danych\n",
    "3. Skąd wziąć dane?\n",
    "4. Przygotowanie danych\n",
    "5. Zadanie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Motywacja\n",
    "- Zasada \"Garbage in - garbage out\"\n",
    "- Im lepszej jakości dane - tym lepszy model\n",
    "- Najlepsza architektura, najpotężniejsze zasoby obliczeniowe i najbardziej wyrafinowane metody nie pomogą, jeśli dane użyte do rozwoju modelu nie odpowiadają tym, z którymi będzie on używany, albo jeśli w danych nie będzie żadnych zależności\n",
    "- Możemy stracić dużo czasu, energii i zasobów optymalizując nasz model w złym kierunku, jeśli dane są źle dobrane"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Źródła danych\n",
    "- Gotowe zbiory:\n",
    "  - Otwarte wyzwania (challenge)\n",
    "  - Repozytoria otwartych zbiorów danych\n",
    "  - Dane udostępniane przez firmy\n",
    "  - Repozytoria zbiorów komercyjnych\n",
    "  - Dane wewnętrzne (np. firmy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Źródła danych\n",
    "- Tworzenie danych:\n",
    " - Generowanie syntetyczne\n",
    "   - np. generowanie korpusów mowy za pomocą TTS (syntezy mowy)\n",
    " - Crowdsourcing\n",
    " - Data scrapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Otwarte wyzwania (shared task / challenge)\n",
    "- Kaggle: https://www.kaggle.com/datasets\n",
    "- EvalAI: https://eval.ai/\n",
    "- Gonito: https://gonito.net/list-challenges - polski (+poznański +z UAM) Kaggle\n",
    "- Semeval: https://semeval.github.io/ - zadania z semantyki\n",
    "- Poleval: http://poleval.pl/ - przetwarzanie języka polskiego\n",
    "- WMT http://www.statmt.org/wmt20/ (tłumaczenie maszynowe)\n",
    "- IWSLT https://iwslt.org/2021/#shared-tasks (tłumaczenie mowy)\n",
    "- CNLPS - Challenges for Natural Language Processing - https://fedcsis.org/sessions/aaia/cnlps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Repozytoria/wyszukiwarki otwartych zbiorów danych\n",
    "- Huggingface Datasets: https://huggingface.co/datasets\n",
    "- Papers with code: https://paperswithcode.com/datasets\n",
    "- UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/ (University of California)\n",
    "- Google dataset search: https://datasetsearch.research.google.com/\n",
    "- Zbiory google:https://research.google/tools/datasets/\n",
    "- Otwarte zbiory na Amazon AWS: https://registry.opendata.aws/\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Otwarte zbiory\n",
    "- Rozpoznawanie mowy:\n",
    "  - https://www.openslr.org/ - Libri Speech, TED Lium\n",
    "  - Mozilla Open Voice: https://commonvoice.mozilla.org/\n",
    "- NLP:\n",
    "  - Clarin: https://clarin-pl.eu/index.php/zasoby/\n",
    "  - NKJP: http://nkjp.pl/\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Crowdsourcing\n",
    "- reCAPTCHA\n",
    "<img src=\"img/ReCAPTCHA_idea.jpg\">\n",
    "<img src=\"img/cat_captcha.png\">\n",
    "\n",
    "<sub>Źródło: https://pl.wikipedia.org/wiki/ReCAPTCHA#/media/Plik:ReCAPTCHA_idea.jpg</sub>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Amazon Mechanical Turk: https://www.mturk.com/\n",
    "<img src=\"img/Tuerkischer_schachspieler_windisch4.jpg\">\n",
    "\n",
    "<sub>Źródło: https://en.wikipedia.org/wiki/Mechanical_Turk#/media/File:Tuerkischer_schachspieler_windisch4.jpg</sub>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Licencje\n",
    "- Przed podjęciem decyzji o użyciu danego zbioru koniecznie sprawdź jego licencję!\n",
    "- Wiele dostępnych w internecie zbiorów jest udostępniana na podstawie otwartych licencji\n",
    "- Zazwyczaj jednak ich użycie wymaga spełnienia pewnych warunków, np. podania źródła\n",
    "- Wiele ogólnie dostępnych zbiorów nie może być jednak użytych za darmo w celach komercyjnych!\n",
    "- Niektóre z nich mogą nawet powodować, że praca pochodna, która zostanie stworzona z ich wykorzystaniem, będzie musiała być udostępniona na tej samej licencji (GPL). Jest to \"niebezpieczeństwo\" w przypadku wykorzystania zasobów przez firmę komercyjną!\n",
    "- Zasady działania licencji CC: https://creativecommons.pl/\n",
    "- Najbardziej popularne licencje:\n",
    " - Przyjazne również w zastosowaniach komercyjnych: MIT, BSD, Appache, CC (bez dopisku NC)\n",
    " - GPL (GNU Public License) - \"zaraźliwa\" licencja Open Source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Przykład \n",
    "- Za pomocą standardowych narzędzi bash dokonamy wstępnej inspekcji i podziału danych\n",
    "- Jako przykładu użyjemy klasycznego zbioru IRIS: https://archive.ics.uci.edu/ml/datasets/Iris\n",
    "- Zbiór zawiera dane dotyczące długości i szerokości płatków kwiatowych trzech gatunków irysa:\n",
    " - Iris Setosa\n",
    " - Iris Versicolour\n",
    " - Iris Virginica\n",
    "  \n",
    "<img src=IUM_02/iris.png/>\n",
    "\n",
    "<sub>Źródło: https://www.kaggle.com/vinayshaw/iris-species-100-accuracy-using-naive-bayes<br>\n",
    "Licencja: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)</sub>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pobranie danych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pobieranie z Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting kaggle\n",
      "  Downloading kaggle-1.5.13.tar.gz (63 kB)\n",
      "\u001b[K     |████████████████████████████████| 63 kB 558 kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: six>=1.10 in /home/tomek/miniconda3/lib/python3.9/site-packages (from kaggle) (1.16.0)\n",
      "Requirement already satisfied: certifi in /home/tomek/miniconda3/lib/python3.9/site-packages (from kaggle) (2022.12.7)\n",
      "Requirement already satisfied: python-dateutil in /home/tomek/miniconda3/lib/python3.9/site-packages (from kaggle) (2.8.2)\n",
      "Requirement already satisfied: requests in /home/tomek/miniconda3/lib/python3.9/site-packages (from kaggle) (2.27.1)\n",
      "Requirement already satisfied: tqdm in /home/tomek/miniconda3/lib/python3.9/site-packages (from kaggle) (4.64.0)\n",
      "Collecting python-slugify\n",
      "  Downloading python_slugify-8.0.1-py2.py3-none-any.whl (9.7 kB)\n",
      "Requirement already satisfied: urllib3 in /home/tomek/miniconda3/lib/python3.9/site-packages (from kaggle) (1.26.9)\n",
      "Collecting text-unidecode>=1.3\n",
      "  Using cached text_unidecode-1.3-py2.py3-none-any.whl (78 kB)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/tomek/miniconda3/lib/python3.9/site-packages (from requests->kaggle) (3.3)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /home/tomek/miniconda3/lib/python3.9/site-packages (from requests->kaggle) (2.0.4)\n",
      "Building wheels for collected packages: kaggle\n",
      "  Building wheel for kaggle (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for kaggle: filename=kaggle-1.5.13-py3-none-any.whl size=77733 sha256=83eee49596c7c76816c3bb9e8ffc0763b25e336457881b9790b9620548ae7297\n",
      "  Stored in directory: /home/tomek/.cache/pip/wheels/9c/45/15/6d6d116cd2539fb8f450d64b0aee4a480e5366bb11b42ac763\n",
      "Successfully built kaggle\n",
      "Installing collected packages: text-unidecode, python-slugify, kaggle\n",
      "Successfully installed kaggle-1.5.13 python-slugify-8.0.1 text-unidecode-1.3\n",
      "Requirement already satisfied: pandas in /home/tomek/miniconda3/lib/python3.9/site-packages (1.5.3)\n",
      "Requirement already satisfied: numpy>=1.20.3 in /home/tomek/miniconda3/lib/python3.9/site-packages (from pandas) (1.24.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/tomek/miniconda3/lib/python3.9/site-packages (from pandas) (2022.7.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /home/tomek/miniconda3/lib/python3.9/site-packages (from pandas) (2.8.2)\n",
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     ]
    }
   ],
   "source": [
    "#Zainstalujmy potrzebne biblioteki \n",
    "!pip install --user kaggle #API Kaggle, do pobrania zbioru\n",
    "!pip install --user pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    " - Pobierzemy zbiór Iris z Kaggle: https://www.kaggle.com/uciml/iris\n",
    " - Licencja to \"Public Domain\", więc możemy z niego korzystać bez ograniczeń."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading iris.zip to /home/tomek/repos/aitech-ium\r\n",
      "\r\n",
      "  0%|                                               | 0.00/3.60k [00:00<?, ?B/s]\r\n",
      "\r\n",
      "100%|███████████████████████████████████████| 3.60k/3.60k [00:00<00:00, 438kB/s]\r\n"
     ]
    }
   ],
   "source": [
    "# Żeby poniższa komenda zadziałała, musisz posiadać plik ~/.kaggle/kaggle.json, zawierający Kaggle API token.\n",
    "# Instrukcje: https://www.kaggle.com/docs/api\n",
    "!kaggle datasets download -d uciml/iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  iris.zip\r\n",
      "  inflating: Iris.csv                \r\n",
      "  inflating: database.sqlite         \r\n"
     ]
    }
   ],
   "source": [
    "!unzip -o iris.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Inspekcja\n",
    "- Zanim zaczniemy trenować model na danych, powinniśmy poznać ich specyfikę\n",
    "- Pozwoli nam to:\n",
    "  - usunąć lub naprawić nieprawidłowe przykłady\n",
    "  - dokonać selekcji cech, których użyjemy w naszym modelu\n",
    "  - wybrać odpowiedni algorytm uczenia\n",
    "  - podjąć dezycję dotyczącą podziału zbioru i ewentualnej normalizacji\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Podstawowa inspekcja za pomocą narzędzi Bash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151 Iris.csv\r\n"
     ]
    }
   ],
   "source": [
    "!wc -l Iris.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species\r\n",
      "1,5.1,3.5,1.4,0.2,Iris-setosa\r\n",
      "2,4.9,3.0,1.4,0.2,Iris-setosa\r\n",
      "3,4.7,3.2,1.3,0.2,Iris-setosa\r\n",
      "4,4.6,3.1,1.5,0.2,Iris-setosa\r\n"
     ]
    }
   ],
   "source": [
    "!head -n 5 Iris.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```less Iris.csv```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Inspekcja\n",
    "- Do inspekcji danych użyjemy popularnej biblioteki pythonowej Pandas: https://pandas.pydata.org/\n",
    "- Do wizualizacji użyjemy biblioteki Seaborn: https://seaborn.pydata.org/index.html\n",
    "- Służy ona do analizy i operowania na danych tabelarycznych jak i szeregach czasowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  Downloading matplotlib-3.7.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.6 MB)\n",
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      "Collecting contourpy>=1.0.1\n",
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      "\u001b[?25hCollecting pyparsing>=2.3.1\n",
      "  Using cached pyparsing-3.0.9-py3-none-any.whl (98 kB)\n",
      "Collecting fonttools>=4.22.0\n",
      "  Downloading fonttools-4.39.0-py3-none-any.whl (1.0 MB)\n",
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      "\u001b[?25hCollecting cycler>=0.10\n",
      "  Downloading cycler-0.11.0-py3-none-any.whl (6.4 kB)\n",
      "Collecting pillow>=6.2.0\n",
      "  Downloading Pillow-9.4.0-cp39-cp39-manylinux_2_28_x86_64.whl (3.4 MB)\n",
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      "\u001b[?25hCollecting kiwisolver>=1.0.1\n",
      "  Downloading kiwisolver-1.4.4-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB)\n",
      "\u001b[K     |████████████████████████████████| 1.6 MB 1.0 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: zipp>=3.1.0 in /home/tomek/miniconda3/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib!=3.6.1,>=3.1->seaborn) (3.15.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/tomek/miniconda3/lib/python3.9/site-packages (from pandas>=0.25->seaborn) (2022.7.1)\n",
      "Requirement already satisfied: six>=1.5 in /home/tomek/miniconda3/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n",
      "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib, seaborn\n",
      "Successfully installed contourpy-1.0.7 cycler-0.11.0 fonttools-4.39.0 kiwisolver-1.4.4 matplotlib-3.7.1 pillow-9.4.0 pyparsing-3.0.9 seaborn-0.12.2\n"
     ]
    }
   ],
   "source": [
    "!pip install --user pandas\n",
    "!pip install --user seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "        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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>2</td>\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>3</td>\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</td>\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</td>\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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0      1            5.1           3.5            1.4           0.2   \n",
       "1      2            4.9           3.0            1.4           0.2   \n",
       "2      3            4.7           3.2            1.3           0.2   \n",
       "3      4            4.6           3.1            1.5           0.2   \n",
       "4      5            5.0           3.6            1.4           0.2   \n",
       "..   ...            ...           ...            ...           ...   \n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "0       Iris-setosa  \n",
       "1       Iris-setosa  \n",
       "2       Iris-setosa  \n",
       "3       Iris-setosa  \n",
       "4       Iris-setosa  \n",
       "..              ...  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "iris=pd.read_csv('Iris.csv')\n",
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "count   150.000000     150.000000    150.000000     150.000000    150.000000   \n",
       "unique         NaN            NaN           NaN            NaN           NaN   \n",
       "top            NaN            NaN           NaN            NaN           NaN   \n",
       "freq           NaN            NaN           NaN            NaN           NaN   \n",
       "mean     75.500000       5.843333      3.054000       3.758667      1.198667   \n",
       "std      43.445368       0.828066      0.433594       1.764420      0.763161   \n",
       "min       1.000000       4.300000      2.000000       1.000000      0.100000   \n",
       "25%      38.250000       5.100000      2.800000       1.600000      0.300000   \n",
       "50%      75.500000       5.800000      3.000000       4.350000      1.300000   \n",
       "75%     112.750000       6.400000      3.300000       5.100000      1.800000   \n",
       "max     150.000000       7.900000      4.400000       6.900000      2.500000   \n",
       "\n",
       "               Species  \n",
       "count              150  \n",
       "unique               3  \n",
       "top     Iris-virginica  \n",
       "freq                50  \n",
       "mean               NaN  \n",
       "std                NaN  \n",
       "min                NaN  \n",
       "25%                NaN  \n",
       "50%                NaN  \n",
       "75%                NaN  \n",
       "max                NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-virginica     50\n",
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[\"Species\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris[\"Species\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>PetalLengthCm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>1.464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>4.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>5.552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 PetalLengthCm\n",
       "Species                       \n",
       "Iris-setosa              1.464\n",
       "Iris-versicolor          4.260\n",
       "Iris-virginica           5.552"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[[\"Species\",\"PetalLengthCm\"]].groupby(\"Species\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Species'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris[[\"Species\",\"PetalLengthCm\"]].groupby(\"Species\").mean().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f97eed545b0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 474.35x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R4lRVYWPlJl5c/k8CuoaCwrV9xzPENViSrTihydHdzDTgrfkbQ0kW4OuV+9hb7I75tovK60JJFsDn13jhwzXUeqU6k2h5VVoFr6x8m4AePB51dP699mPK/KUtHJkQsSWJtpl5fBrb95ZHtJdWemK+7fLqyG2UVXmo8fpjvm0hGlLtq6HOH36M6rpOhbeqhSISIj4k0TYzq0nltJ4ZEe1ZqfaYbzs92RYxEk/bjAScNlPMty1EQ5LMTpIszrA2o2okzZrSQhEJER+SaJuZAlx1brdQTWGjQeW6C3uSnWqL+bbTnRYmX9kf6/4xYDNSbNx+RT9MceqIJcTR2HBwx5AbSbYG/zYcJjt3DLmBRDWphSMTIrYUPZqK8seRkpJqNC1+v5LL5aSoKPLWl0/TKav2YjGpce35qyhQWevHr4PdpMa8A9axONI+a2kSV+McS1yKArXUUOmrIsHowKE4oxrUItZxxcuRYnO5nPXMLU4U0us4RkyqQkbi/qHl4ngqo+vgtBpb9ZeNOHnpOlixYzXa9/98Qp3nC1Gv1ne5I4QQQpxAJNEKIYQQMdSoW8crVqxgw4YN1NTUhLVPmjSpWYMSQgghThRRJ9q//OUvzJ8/n0GDBmGxWELtyuHvkwghhBAiJOpEO3v2bGbPnk1mZuYxbejWW29l7969qKqK3W7nwQcf5JRTTgmbZ8aMGbzzzjtkZATfQz3ttNOYOnXqMW3vZOX2BthTWM2yzUVkpznISbNjPORcqMTtZVd+sJNUhywnaQ5zaJqmQ2FFHfmlNSQ6zLRJszdbr+WArlNQXkdBaQ0ZZbW4Eq2YDXKSJoQ48UWdaLOysjCbzQ3PeARPPPEETmewC/vChQu57777mDVrVsR848aNY8qUKce8nZNZrT/Avxds5scN+aG2Wy7rw/BemQQCGgUVdTz+1goq3V4AEh1m/t91g8hKsqKqCqu3lfD8+6tDy47om8215/fAbGhaslVU+HF9Ia9+uj7Udt7Q9lx5ThcMckdECHGCi/ob9LHHHuPBBx9k/vz5LF++POxfNA4kWYDq6mq55RwDe4vcYUkW4D/zN1Ja7cFoVPn+59xQkgWodHv5fk0uRqNKVZ2f1z9bF7bs4p/zyC+rbXJcFTV+/jV3Y1jbF8t2U1he1+R1CyFEaxf1Fe369ev59ttvWb58OVarNdSuKApff/11VOu4//77Wbx4Mbqu89prr9U7z9y5c/n+++9xuVzccccdDBgwINoQAUhLS2jU/M2htbxsvnpnZHF2d50fb0AnJcVBblHkwAa5RW5SUhxU1lXirousiezxaU3+/cr3lOP1Rw5s4A3orWbfHdDa4jlA4mqc1hoXtO7YRGxEnWifffZZZs6cyfDhw495Y4899hgAn3zyCU8++SSvvvpq2PQJEyYwadIkTCYTixcv5tZbb2XevHmkpERfC7W1VIZqCdlpDowGFX/gYFLr0T6FJIeJoqIqhvXJZtWWorBlhvXNpqioCosBenVKZcMhydpoUElPtDb593OYVdplJrCnoDrUZrMYSUkwt5p9B63rszyUxNU4rTUukMpQJ6uobx3bbDYGDRrULBsdN24cy5Yto6ysLKzd5XJhMgUL4I8YMYLs7Gy2bt1a3ypEPbKTLUy5dhDZ6Q4A+nd3cdOYUzHvv01/SocUrhrdHZvFiNVs4KrR3ejVIXgSY1AUbh5zKoNOCXZEy3E5uP+GwaQmNH1AApOqcOdVA+jbNR0IdsK6//rBJMlgB0KIk0DUV7STJ09m+vTp3HbbbaSlpYVNU9Wj52u3201lZSXZ2dkALFq0iKSkJJKTk8PmKygoCPVq3rhxI/v27aNTp07RhnjS0zTokpXAgzcMwRfQcJgNYWdSDrOBi4a0ZUSfbHQg2WZAO+SObpLNxKSxvam5MIDFqGJSlWYbMD7VYWLyFX2p9QZIS7FT5/ZI+T0hxEkh6kR73333AfDee++F2nRdR1EUNm7ceKTFAKitreXOO++ktrYWVVVJSkpi5syZKIrCzTffzOTJk+nTpw/PPPMM69evR1VVTCYTTz75JC6X6xh/tZOX1aDQLiu53ltUmgZOiyH0/8OpQML+0X+am0ow2TvtZurcsR+fVwghWoOoR+/Zt2/fEae1adOm2QJqqpP5Ge2hWmtc0Hpjk7gaR+JqPHlGe3Jq8IpW13Xcbne9ybS6uhqHwxGTwIQQQogTQYOdof71r3/x8MMP1ztt2rRp/Pvf/27umIQQQogTRoOJdtasWdx+++31Trv99tv5+OOPmz2oE0VTinKoavBfLChK8N+Rt906446lpnxWwf155OWlOIsQJ7cGbx3n5ubSsWPHeqd16NDhqM9uT1YBXWdfSQ3rd5aS6rTSs0MKSbbo+p2pKuwrrWPdjhJ8/gB9uqTTLs0e9bbLa3xs2l1O7vc76dE+hS7ZTqymYOcmRYGCCk/oXdlTO6WSkWQJ9Sz2BTR2FVazeXc52ekOurdNIsES/QBPe0pqWLu9GJPRQO/OabRJtdbb4ao1MeDHVLkH754NVCQkYc3uSZ05PaplFQXKAiVsKt1Gnd/DKendyDRlgR5MrAHFT25dLptLtpFqS6ZbShecJMXy1xFCtEINfosaDAaKi4tJT4/88ikuLm7w1Z6TjaLAxl0VPPPuylBbepKVB28cgjOKpLWvtI6/vLGMOm8AgI+/3s4DNwyho6vhZ+HV3gAvfLSGHfsqAZjDTq48txsXD2uP5tfJL6/joVd/wLe/SpPRoPLIxNPJSrKiqArfr8nn3/M3hdbXtW0yd03ojzWKgQV+KXLz6Js/hjqiWc0GHrhhKDkp1gaWbFnGok0UzXo69LPBkUzqlQ9SZ047ylJBpYESHlv8HLW+YClJRVH4fyNup625XbA3fsUm/rHirdD8Lnsafz79VhxIxxchTiYNfoMOHTqU119/vd5pb775JqeffnqzB3U88/g1/j0//HWn4oo6dh9SFelIDAaV5RsLQkkWQNN05i7ZiRpFYf+9hdWhJHvAp99sp7jSi8Gg8vXKfaEkC+APaHy9ci8Gg0pVrY/3/rclbNlte8vJKwkfe7jeuE0qc5fsDOvtXecNsHxjPsZmGv0nFsx4qPj23bC2gLucQOGOBpdVFNhQsiWUZCHYcXD2li9QDDoepZZ31n0StkxRTQn7qnObJXYhxPGjwUus//u//+PXv/41O3fu5Pzzz8flclFUVMTnn3/OqlWrwt6rFcGh5mrqfBHtXl+gnrnDqSpU10QuW19bfTy+yPu0voBGIKChKFBRz7urFdVeFAX8AR1foJ7l/Q3Hja7gro2MsarGd9RnwS1N0QJonsgTCc3X8GAHiqLg9kYuW+WtRkNDI0CtL3JABq8W3WcphDhxNHi50alTJz788EOcTid/+9vfmDRpEn/7299wOp18+OGHR3x+e7KymVQuObNzWJvREKz12xCfT2PIqZHj/f5qSHu0epLg4dplOEg4rKzh4F6ZpDkt+P0aowa2i1jm3EHt8Ps1kuwmzuibEzbNYTOFyjkeTcAfYPTg9hHtQ0/NwldP8m8tfEYHzsEXhzeqBkwZnetf4BCaptMnoycK4WcSF3YZhaoZsOHggq4jw6aZVCNtErKbHLcQ4vgSdcGK40VrKFhR69NYsamQz5ftIiPFzhWjutIm1RZVOUM/sHlPOZ9+swOvP8DFwzvRp1MqVlPDt2APdKT69Nvt7C6oYkivLM4e0IYUezD5BjSdzfsq+Pjrbeg6XD6yKz3aJGHY38u42hPg2zX7+H5NLp1zkhh3Vhdcieao4q7zaaz9pZS5i3diMqqMO7sLPdokU9+d49ZUUMAccBPYtQL3ys8xOFNxnj4eT1IndBq+FNcVjd21u5i1eQE1vlou7nouvVJ6YtItANQqNfxUsIqvfllCpsPF2B7nk2XKbnRZy9a0vw4lcTWeFKw4OTUq0e7YsYNNmzZRUxN+y+yKK65o9sCOVWtItBC8tejTNIyqyrEUDNb2/zOr9ZdKPCpVBYOCGgjUu6yuBEOqL3UrCvgCYDQAjQxbVcGrBdd7tNOC1vZFqCgKRt1DYnICJeXehhc4jG4IoOs6Bt0Y8VGrqoIPLwbFAIFje17d2vbXARJX40miPTlF/e7GzJkzefHFF+nZs2fEeLStKdG2FrquY1SUY0qycDBZHdPrMZqGK+3IXzaKzhGv13Sd4FXoMYStaY04oFoRXdfxYUY1WYDGJ1olYECh/l2maToGZJQiIU5mUX8v/utf/+KDDz6gZ8+esYxHCCGEOKFEfS/LarXSuXPDnUSEEEIIcdBRE62maaF/d955J48++iiFhYVh7VprL/0jhBBCtKCj3jru1atXqE7rgT5TH3zwQWh6tOPRivhSVWX/Z1P/I+IDRST8fjlJigeLxYCm6XF/1cloVDEYFHy++jvFCSHi46iJ9ssvv4xXHKKZlNf4WPxzHpv3lDGibw59O6di21/rWEdnT3ENn/+wG4DzT29Pu3R7xLugonkEqGRz6Va+3reSNvZ0zm4/hFRLm7gkvWK9gCU7VrC7Yh+DcvrRJ70nDl3qLAvREo6aaA8dg/b111/npptuipjnzTff5IYbbmj+yESjub0Bpv9rOcUVwcpG67aXcMGwDlx1TlfQg0n24deWheZfui6Ph38/lPZRFKUQjWM0qize8xPvbJwPwHq28n3uaqYOv5UEQ2RRkuZUQSnPLn2V8rpgOc6NRdsY3fkMLu9yKVpATqqEiLeoO0O9+OKL9bb/4x//aLZgRNPkFrtDSfaAL5btpqLGh9Go8uWKvRHLLFy+p1XXIz5e1fnLmLXtq7C2Gl8teypjP9rV3qrcUJI94KudSygJlMR820KISA2+3rN06VIg2DHqhx9+4ND6Fnv37sXhkKuh1qK+cU8VQEFH1wlVgDqUQVWO9VVfcTQKqErkCUw8xqat9zhQVHlEIFq1iy++mIceeoihQ4e2dCjNrsFEe//99wPg8Xi47777Qu2KouByuXjggQdiF51olJx0O1lpDvJL3KG2i0Z0JNFuIhDQGDWoLd+s2htKrIoSrHUciKKOsmgcqyGFy7udy1vrZ4faEswO2ie2OcpSzaOtM4c0WwoltWWhtl91OZNUYxqaP+abFyeIFStW8PTTT7N161YMBgOdO3fmvvvuo2/fvjHZ3ty5c2Oy3tYg6hKM99xzD08++WSs42my1lKCsaVU1PpZsamQbXvLGdori54dkkPjyepAbmkt36zei67BOae1JSfVFvfrnNa2zw5o7rgCVLOjYgdLcteQ40hjaE5/Eo05DS/YDHGV6IX8lPczeyr2MSC7Dz2SuzX7OLgny+fYnI6XEozV1dWcc845PPzww1x44YX4fD5WrFhBenq6FC06BjKoQBO1xj9qRVFIT084YlyG/WPbttSVbGvcZxC7uCwWI4GAdsyvUx1rXAaDitGo4vHE5jL2ZPscm8PxkmjXrl3LDTfcwIoVKyKmffzxx7z//vuceuqpfPLJJ7hcLqZOncqwYcMAqKqq4vHHH+fbb79FURTGjx/P5MmTMRiCbz+8//77vPnmm+Tn55Odnc1TTz3FqaeeyqhRo3j00UcZPnw4mqbx2muv8f7771NVVcXpp5/OtGnTSE5OxuPxcP/99/Pdd98RCATo0KEDL7/8Munp6XHdR41x1FvHZ599dlTPlL7++uvmikc0g4bOneRWcXzFKtE1JLB/LGIhGqtTp04YDAamTJnCRRddRP/+/UlKOvh62M8//8wFF1zADz/8wP/+9z9uv/12vvzyS5KTk5kyZQrp6el88cUX1NbWcsstt5Cdnc2ECROYP38+M2bM4MUXX6RPnz7s3r0bozEyDb311lssXLiQ//znP6SmpvLoo4/yyCOP8MwzzzBr1iyqq6v5+uuvMZvNbNy4Maz+fmt01ET71FNPhf6/du1aPvnkE6699lpycnLIzc3lP//5D+PGjYt1jEIIIeIoISGBd955h1dffZUHH3yQ4uJizjrrLB599FEAUlNT+d3vfoeiKFx00UW88cYbfP3115xxxhl8++23rFixAqvVit1u5/rrr+e9995jwoQJfPjhh/z+978PPeft0KFDvdt/7733eOihh8jKygLg9ttvZ+TIkfj9foxGI+Xl5ezatYuePXvSu3fv+OyUJjhqoh0yZEjo/4888givv/46mZkH3wE866yz+P3vf8+NN94YuwiFEELEXZcuXfjrX/8KwPbt2/nzn//M9OnTOeOMM8jMzAy725mTk0NhYSG5ubn4/X7OOOOM0DRN08jOzgYgLy+P9u3bN7jt3NxcbrvtNlT1YM99VVUpKSlh7Nix5Ofnc9ddd1FZWcmYMWP44x//iMnUekfJinr0nsLCQux2e1ib3W6noKCg2YMSQgjRenTp0oXx48fz3nvvccYZZ1BQUBAqwQvBBDpq1CiysrIwm8388MMP9d4Szs7OZvfu3Q1uLysri+nTpzNw4MB6p99+++3cfvvt7N27l4kTJ9KpUyeuvPLKpv2SMRR1pYJRo0bxhz/8gcWLF7N9+3a+//57brvtNkaNGhXL+FqWAp6AHnwPppFUVcEb0DjSEzJdhRqf1ohPIHq6AuVVdXF5Z/N4YVJ8mPVa1HreJW5JBkMATalCNUQ+V1cUCKg+qjzuYzkEj0pRIGDwEVC99a5bUcGr1qGr9R/BJsWPv7r8mOI6sG7UE6of5gll+/btvPHGG+Tn5wPBRDpnzhz69esHQGlpKW+99RY+n4/58+ezfft2zj77bDIyMhgxYgR//etfqa6uRtM0du/ezY8//gjAFVdcwRtvvMG6devQdZ1du3axb19kEZerr76av//976FppaWlLFy4EIAffviBzZs3EwgESEhIwGg0hjpatVZRX9FOmzaNGTNmMHXqVAoLC3G5XFx44YXcfvvtsYyvxZTV+Pjkm+2s3V5Cv27pjD2zM8n26G5N1HgDfL82jy+W7SY92cpvz+tJu/SDdwPyK+r4cNE2duRW0L9bOhcP70RagrnJMSsKFFR4eP/LrezIreCMfjmcN7g9CZbWfRDGkoqGqXQrld+9R6CmAufACzF1OR2fIaFl41KhxLuPj9Z9zo7KXIZknML5nc/CproA0BQ/W6q28vHG+fg1P2N7nE+vlFMw6U0/TvyKlw3lm/lk8wJURWV8zwvpkdgNgx48vqup5Mud37Fs3yo6Jbflsh4X4TJmoO8/57RU/kLl9+9RUZaPo89ILKecjccYXR3lSsr5fNtXrMpfT/e0ToztfgEpSlqTfyfRvBISElizZg1vvvkmVVVVOJ1ORo4cyT333MMXX3xB37592bVrF6effjrp6ek8//zzpKSkAPDkk0/y9NNPc9FFF+F2u2nXrh0333wzABdeeCHl5eX86U9/orCwkDZt2vDkk0+GlfsFuO6669B1nRtvvJHCwkLS0tK46KKLGD16NMXFxUydOpWCggLsdjsXXXQRY8aMifs+agx5vacedX6Naa8vo6CsNtTWLiOB+383GLMh/BT+8O76iqowZ+kuPvpqW6jNoCpM/8NwXE4L5XV+pr32AxXV3tD0bu2S+dOE/pgNTbu8razzc+8/llB7SC/XIb0yuWXMqa2qJlA8X7+wVu+h6J2HCL5FHJQ88hr0Hr+KOE7iGVedXsLUxS9Q7T1YXOTUtM7cOuB60KzsqtvJk0vCy5veMeRGeiY0/R3GLe7NPLfs9bC2u4dNopOtM7rq59V1/2FN/obQNIfJzkNn3kUCiVhrCyh6534IHDzGEk67AOOQXxPQjn6UBVQvf//pFXaUHbx1mGxN5IERf8SmN1+FOXm9J7Y+/vhjPvjgA959992WDuW4cdRv9uXLl4f+v3Tp0iP+O9EUldeFJVmAPYXVEXWE61PjCTBvyS9hbQFNZ09BNQC5Re6wJAuwdU85hRWepgUN5BW7w5IswI8bCqio8TV53ccrX/42Dk2yAFU/LcAYqGmZgPbLr8oPS7IA60t2UOEpwWBQ+WHfyohlvtz5HQZD006ZjEaVRb8sjmhfvGc5RqNKhb8yLMkCuH01FNQUAhAo3RuWZAGqVy/E6AuvrVyfUm95WJIFKK+rpKi2uLG/hhDHlaPeOp42bRpz5swBDpZiPJyiKCfccHpmU/3nH2ZTw7dgjQaFRIc5IuFZzcFlLfWsw6AqWI6wzcaob90WkwFjE7+cj2eKxR7RZrAloqvGw/NvXJkNkbeAjaoRk2pC13VSrJG3YlNtyU3erq5Dmi2l3nVrmo5RMWIymPAFwk/ODsSrmCwRyxpsDlAafgplUo0oihLxnrfZ0Hp7iwrRHI767X4gyQIsWrSo3n8nWpIFSE+0MGpQu7C2i4Z3JNXZ8PMxs0HhdxedEtaWleagfWbwmWBOup0B3V1h0y85oxOupMgvsMbKTrNzaqfw512/Ob8HibaoH8WfcIyZXTEkpB7SopB45q/x6S375Z6VkE2/9G5hbZd1G0mCKR1N0xmU3R+r8eAxYVKNnNvxTAKBpp0dBAIa53QYjumQ5GYxWhja5jQ0TcdpSOSKUy4KW6Z3RncyrcHX+pTU9pjSw/82ks65Dm8Uz7yTjSlc2m10WNvQNgNIt7iOsIRojcaPHy+3jRupwWe0N998M4MHD2bw4MH06dOn3i7brUlzlWCs82vsLqhmX1E1bTMSaJ+RgKWe4eTqe+ai65BbVsu2veUkOSx0aZOI03pwv1XW+dmRW0l+iZv2WU46ZiVib4YrWgiOSftLfhXFFbW0z3TSLt2BsZX1tI33MzSrtwR/wTb0OjfGrK74nG3R6jnHjHdcPq2MnZW7KXAX0yGpDW0T2qMSvAJXFCgNlLCjfBe6qtPJ2Z50o6tZRlpSFIWSQBHby35BURS6JHck1ZAWWrdf8bKvNpfdlXtx2dPpkNAOGwefoVp85WiF26C2AtXVCX9SewJR9qv0Use+2lz2VuWS6cigfUJbrHrkXYemkGe0orVpMNHOnDmT5cuXs3r1agKBAP369WPw4MEMGjSIAQMGYLE0/UqsOTV3rWNF4ahfbkf7o25oWVUFLUYV8o7HL5tYa8pnGUsNHQcn4v5qaN1NcTwe+5JoT2wNnoZOmjSJSZMmoWka69ev56effmLFihW88847VFVV0bt37xP6NkJTvgwaWjZWSVbUr7X2r2+tx0Es91dr/SyEiIWo7wOrqkqfPn3o2LEjHTp0oH379nz66ads3bo1lvEJIYQQx7UGHwyWlpby+eef8+ijjzJ27FguueQSPvnkE7KysnjllVdCFT+EEEKcOEaNGsWWLVsi2gsKCrj22mtjvv2FCxfy888/x3w78dDgFe3w4cPp0qUL1113Hdddd11UBaGFEEKcePx+P5mZmfz73/+O+bYWLlxI7969QyP9HM8aTLR33nknK1as4LnnnmPOnDkMHDgw1BHK4Wi+ai4nGh2oqvNjMqjYzYYGx4g9lKpCVV0AXddJsEa+7+kHyqo9WEwGUuxGAoFmDV3EiaKAR6nDE/DgMDhQtebr0W8wgC9QjsdfS4I5mYBma9TymqGWCl8FdqMNu5Ikx1gr9fVPe3hr/kaKy2pJT7Fx3YWncM7Adg0v2AjXXnstAwYMYM2aNVgsFh566CEuv/xyli1bRm1tLVOmTGHbtm0YjUY6derEc889F7GOHTt2cO+991JbW4umaVx22WXcdNNNeL1enn32WZYvX47P56N79+48/PDDrFy5kkWLFrFkyRI++OADbrjhBsaNG8crr7zCZ599BkCfPn144IEHcDgcLFy4kOeeew5VVQkEAjz44IMMHTqUN954g7lz5xIIBLBYLDz88MOccsopEfHFWoN/2X/4wx+A4FBHGzZsYMWKFfz3v//l//2//0dGRgaDBg3ivvvui3mgx5OqOj/vLtzKsvV5OO1mbrikF306phLNWzbegM7iNXm8/+VWAgGNC4Z15MKh7bHtL0ZRVO3l7QUb+XlbCclOC7+7qBf9OkUWIBCtm6LAHs9uXl/5LoU1JXRL7cTv+l3VLHV/FSXA9orNvLZ2FmW1FZya1pnf9R6Hw5AV1fIlWj5vrf6IbaW7SLOn8Lt+l9M1oQt64OStmd0aff3THl74YA0eX/AsqKislhc+WAPQ7Ml2y5YtvP766xiNRvbu3Rtq//7776msrGTevHkAVFRU1Lv8O++8w1lnncVtt90WNt9rr72G0+nkww8/BIJjoL/yyiv88Y9/ZNSoUfTu3ZtrrrkGgG+++YbPPvuM//73vzgcDqZMmcJLL73En//8Z55//nmmTp3KoEGDCAQC1NYGK/uNGzcuNIzrkiVLmDp1Ku+//36z7ptoRP3ypqqq9O7dm8svv5zLL7+cMWPGUFBQEJdbCMcVReHT73fyw7o8dB0q3V6ee281+YeVdDySHXmV/Hv+JjzeAP6Azpzvd7JqazGKErySfffzzfy8rQSA8ioPz7+/ij0lLVtOUDRehVbO00tmUlgT/Cy3lu5k5oq38ClNL8VZ7snlb8vfoqw2+GW2vmQHb6ydhUFt+BgMGNy8tuo9tpXuAqCkpoznl/2TYl9hk+MSzeut+RtDSfYAjy/AW/M3Nvu2Lr300nprKPTs2ZMdO3Ywbdo05s+fj9lcf1GfwYMH89FHH/H3v/+dpUuXkpiYCAQLIX322WeMHTuWsWPHsmjRoiMOo7d06VIuuugiEhISUBSFq666KlQC+PTTT+evf/0rr732Gtu3bychIVhAZd26dfz2t7/lkksu4fHHH2fjxubfN9Fo8Iq2tLSU5cuXs3z5clasWMHWrVvJzMxk0KBB3HnnnQwePDgecR43ar0BFq/JjWjPLXGTk3r023cGg8rKzZFfaF+t3MuIU7OocHtZvbUobJquQ26xm3ZpzfvSv4itotpifFp4mc69VXlU+itJMzStUlJ+dRGaHv7O0KbSnVR6y3AYj34MFteVsrsifNgyv+Ynv7qItMTsJsUlmlfxEU7ej9TeFIePRX5Au3btmDdvHj/88APffvstzz77LLNnz+avf/0rK1cG63U/++yznH/++fTv35/Fixfz6quv8tFHH/H000+j6zpTp05l2LBhDcZw6Pi3h7vvvvvYvHkzP/zwA3feeWfoVvOdd97Jf/7zH0499VQKCgo466yzjn0nNEFUnaE6dOjAoEGDuP766xk8eHDEkEbiIJNRISfdwc688CLrifaGyzdqmka7zMgX1zvnJKIoYDWruJJtFJWH/yE5o1i3aF0STJH9G6xGC1aDtcnrdpoj151ocUa1brvJht1ko8YXfowl1LNO0bLSU2wU1ZNU01Ma9zy+KfLz80lKSmL06NGMGDGCM888k/LycqZOnRo2365du2jXrh3jx4+nQ4cOoceNo0aN4p///CcDBgzAarVSXV1NQUEBXbp0ISEhgaqqg8U9hg8fztNPP821116Lw+Hgww8/ZPjw4UDwGXCPHj3o0aMHNTU1rF27losuugi/3092dvAE8Z133onTXonUYKL97rvvcLmkFmm0jIrC9Zf04i9v/Ig/ELyq6N0ljXYZDX9R6Tr065JOZqqNgtLgH1CCzcR5QzqgaTqJVgO/u7gXz7y7MlT9akAPV1TrFq1LmiWd8zqfzRc7vgm1/a7flSQoziYXc8h2ZDEiuy+L84KvRigo3Nh7LGZjWoO1klNNmfy2z1heXfnfUNuZ7QeTY8+EVlpY42R13YWnhD2jheAgItddGL/OPps3b+Zvf/sbELxQmDhxIpmZmRHzzZ8/n9mzZ2MymVAUJZRoJ06cyAsvvMAVV1yBoigoisLtt99Oly5dGDNmDPfeey8LFiwIXaFu3ryZCRMmANC7d+9QH6K//e1v7Nq1C4PBQGJiIo899hgJCQlMnjyZK664guzs7Ba7moUGSjBGOwReNJf98dLcJRgbUl9JNUWBkmoveSU12MwGctIdWOupk3wkbm+AfUVuAppGW1cCiTbjwS9fRWdfaR25RW4S7Cbauhw4LZHnS8djGbqWFvdax4qXQk8hFZ5KMuzppBrTUfVmqsGsl7OvJp9Kj5vsBBdplmwC0Q6koHrJ8+SRX11CktVJW3sWZj3yTot8jo3X3CUY49HrWDTdURPtqFGjGl5BKxsmrzUk2tagtcYFrTc2iatxJK7Gk1rHJ6ej3jpetGhRvOIQQgghTkjNMzabEEIIIeoVdSma6upqZsyYwfLlyykrKwurdPT111/HIjYhhBDiuBf1Fe3DDz/Mhg0buPXWWykvL+eBBx4gOzub66+/PobhCSGEEMe3qK9oFy9ezLx580hJScFgMDB69Gj69OnDpEmTokq2t956K3v37kVVVex2Ow8++GBEzclAIMCjjz7Kd999h6IoTJw4kSuvvLLRv1S0fJpOSaUHk1ElNcHMoa9Cq6pCRa2PKreXRIcZp9XUqHrFTeHXobC8loCmk5lix3zY6VBdQKO00oPdYiTZYQqrhawoUF7jp/iXEhxmI3azGrexP014Ud1FKIqK3+HCr0dfu9ek+jFV56N7alCSMqlRk8Kmm3UPirsQxWDGb0/Hr0dfDlA1+CirK8IX8JBmz0DRwl+H8qkeSj1l1JRXYFOcKIf0/FVVcPuLqayrINmWjM2QHrfOdn5jHQV1RezN1ciwZWD2h78fadGqwV0CZgc+axqafvAINhig0lOA2+cm1ZaGkfD9GVB9lHnLUBSFFFNKs9ZZDqg+Sr2lGFQDycYUVO3gZ6Uo4KaaSk8lCeYEnEpi3I5PXdUo85Xi0/2kmVMwapaw6cHjoBSLwUKyIRnq6QEuxLGI+q9L0zSczmDPOLvdTmVlJS6Xi127dkW1/BNPPBFafuHChdx3333MmjUrbJ7Zs2eze/duvvjiC8rLyxk3bhzDhg2jbdu20YYZtYpaP8+/v5qdeZUoClwyohMXn94Bs1EFBTbuqeD591dT6/HjsBr5vwkD6JqdGPNkW1nr471F21i6Ng+AUzqmcNOlvUl1BF/NKK7y8tTbP1FUXotBVbjmwp6ccWoWBlVB12H5lmJe+2wdPr9GstPCPb8dSFZy04sgNMTiK6fqy1fx7F4PgKPXmVhPvwqvseHelBatGt+aLyj9cQ7oGsaULNIvuR23Lfi5W70lVHw+E2/eVkAhof+vsAwci9fQ8PvDAb2KOdsWsWDnEnR02ifmcNtpV5OgBt/1q9TLePHHf7K3Mg9FUbi422hGtzsLk25BUXQ2l6/jH6vfx+P34DDZuWPgb+iQ0CPmybacYmatW8CK3OC7sL1c3bi6zzhSCb7Tbq3JpfTTpwlUlYJqJHnkNRg6jyCgmFAUH8vyfuTfG+bi1/ykWJP44+DrSDMFX/twU8Xb6z5iTf4GAAa36c9VPcZgJ6HJcVdTyZur32VT8XYAzuowlLFdL8Sq20P1nZ//8Q3c3hosRgsTT/sNPZ09QY+iEHgTeJQ6Fuz8kv9t/xYdnU7J7Zl42jUkkgxAhV7GC8veILeqAEVRGNvjPM5pcyYmXYrBiKaL+pStZ8+eLF++HIBBgwYxbdo0Hn74YTp27BjV8geSLASf99ZXSmvevHlceeWVqKpKamoqo0ePZsGCBdGGGD0FZi/eEarepOsw+/ud7CqqBqCyxsff/7uKWk+wRJ67zs8z766iss7X/LEcZuPu8lCSBdj4SxmL1+ZhNKoEdHht9rpQZaiApvOvuRspKK8DoKjKwz8+/hmfP1hZoLzKw4wPVuONcVJQVQXf1iWhJAvg3vAdev6m6JYv/YWKZZ/B/rKB/rJ8yr97H4viRVWgdu3C/UkWQKd69RdQvCOqde+p3sP8nYvR91/2767MZe7WrzAYNFB1Zm/9gr2Vwf2t6zpztvyPfbXBEprV/kJeWPkuHn+w/rDbV8OMn96hNlAc1babYlPJ9lCSBdhQtJWf8n7GaDRgxkP5F68EkyyA5qf8y39idOcDUFSXy5vrPsW/v8RjWV0Fr6x6H111oygKa4rWhZIswPJ9q9lYtoUjVLeLmqLAkn3LQ0kW4Ntdy9hR+QsANbh54cc3cXuDtbk9fg8vrXiLCq28aRuOwh73Xr7Y/k3oONhZvpsvd36LouroqsbHm+eRW1UABI+DTzZ9Tn5t3tFWecJr6fFoG+P+++9nxYoVx7z8tddey1dffdWMEYWLOtE++uijodKLDzzwABaLhcrKSp588smoN3b//fdzzjnn8Oyzz/LEE09ETM/LyyMnJyf0c3Z2Nvn5+VGvP1pev86qzUUR7bvzq1EUKKv2RhTrrvX4qaj2NnsshzKZDGzYWRrR/vPWIvyaRq03wJbd5RHTDyTekorIcmx5JTVU18b2BMGgBKjdFnmQe3avx2Bo+BDzV0Ymrro9GzD4qjDqHuq2r4yY7s3fjtrAcEiqGqwffLg1JdvwBqrx6HX8XBh5MpBXHfzCLa0tDSWrA9y+Girq6h+hpLlYrSY2FW2NaF9fuAXFFED1VuMr/CViulYZPKZLaiKPob3VBbi9VRgMCj/lrY2Y/nP+hqg+q6PR1AAr8yPXvbk4+FlVequo8rrDpgW0AKV15U3abkMURWFXxd6I9lUF6/HixaPXsa6e46CgJvYnVMebeI5HW9+2j+Sxxx5j0KBBcYzm6PEcLupbx+3aHaw2kpqayvTp0xsXFcGdAfDJJ5/w5JNP8uqrrzZ6HQ1JS2v49pfXH6BX57SI4v8dspykpzup8esYDQr+Q8rVWUwGMlIduFyR62/Ol827tUvmu9XhRd17dkwhLcWB1eanQ3Yiuw6ro5yZ5sDlclJSE5lQ05KsuFIdpCbG9vZxoFM/vPnhV5m2dj1JTK3/9u6h+6y6KDViuiW7K+bEFOxWB94OvakqLwibbs3uhDOKzzq7PCOirWdKB1KSUlBUIz3Su/DTIVeOADmJmbhcTtxaKgZFJXBIgX6r0UKyPQlXemwLDHRJ7cjyw+LqntaJ5AQnflXHmJqDvzT8+LWkuEh2OSnyRA6bmGFPI8meSEqSg35Zp7CpeFvY9N6ZPUhJaXwpz0M/R03X6JPZkz0V4XF1c3UiLS0Bf3UyNpOVWl9daJqiKGQmpuJKbt79efjfZMfayPrsp2Z0JyM5Gb8WoHtaZ34uCB/ZJcuZHpNCEs25zqp131L21dv4K0swJqaRMvK3OHs3b6nBpo5HW1tbyznnnMP8+fNJTQ3+rf/1r38lISGB22+/nTVr1vD000/jdgdPwiZPnsw555zD3r17ufzyy7nmmmtYsmQJY8aMweVy1Tvu7LXXXsuNN97IyJEjqaqqYvr06axbtw5FURg0aBAPPfQQbrebRx99lLVrgyeDY8aMYeLEiRG/b3FxMVOnTg2NInTTTTcxbtw4IHiVf/nll/PDDz/Qrl27qPNgo3pAfPjhh8ydO5fCwkIyMjK46KKLQjUqG2PcuHE89NBDlJWVkZJy8EshOzub3Nxc+vbtC0Re4UYj2spQl53VhS27Sinaf9v1zP5taJNup6ioCptR4dbL+/HSR2vwB3SMBpXbr+yHWdUjqro0dxWa3p3T6NUpjQ07g8Ontc1I4OwBbSkpCR6Et4ztzWP/Wo57/1Xq+LO7kOY0U1RURbLNyLUX9OQ/n29C18FmMXLHlf3RvD6KimJ7VWvtcQamrT/hKw4enJYOfSD7lCNWwTm03ZragYQ+51C99msAVHsiKWdNoLxGhZparP0voHbXOvz7k62t+xC0tM5R7fcOzvaMyO7H4rzgOJ1p1iTGdR9NZUUACHBZ9wvZUbqLsv1XqWd1GEqWOZuioiqcxnRu6jue13/+mICuYVKN3NL/SqxKWswrD/XO6EG31E5sLd0JQNvEbIa2PW3/dhVSzp9I8cdPontqAIXE4eOps2ZSXVRFuiWTK7qN5qOtX6KjYzNauaX/lQS8weP7tIx+/Ji8mp3lewDontaZXqk9G/071Xfsn9n2dNbkbWBfVfBOVL/MU+ic0JGioioMio1JA6/lhR/fxKf5URWVG/r/Gpu/ef+G6ourja0NQ9sMYNm+VcF57Gmc32kkZftril95yqXsqthHRV3wJHZkx+FkmDKb/XNuzspQVeu+pXjuTPT9jzb8lcUUz50J0OzJtinj0dpsNs4991zmzJnDddddh9/vZ86cOfz3v/+lsrKSqVOn8sorr5CRkUFhYSFXXHEFc+bMAaC8vJwuXbpwxx13AMHkWN+4s4eaPn06drudTz/9FFVVKS0N3uF56aWX0DSN2bNn43a7+fWvf02PHj04++yzw5Z/9NFH6datGy+++CKFhYWMHz+eXr160b17dwCKiooafUUfdaJ98skn+fLLL/nd735HmzZt2LdvH2+88QY7d+7knnvuOeqybrebysrK0CgKixYtIikpieTk5LD5LrjgAj744APOO+88ysvLWbhwIW+//XajfqFopTpMPHzT6RSW12I2GXAlWjAeuBWpQ7/OqTxx6wjKq72kOC2kOExx6R2ZbDNy++V9yCupwa/p5KTbcZgO9trMTrHy+KRhFFfUYbeaSHOaMew/0TEoCmf3z6FPlzTqfBpJdhOJtvj0lq4zp5E07v+hV+WjKAY0ZyYeJbqr6DpDEpZhE7D3OgPdW4OalE2t1RUqYl9ncZF8xUPolQUoBiNaQiZexXL0le5nVJK4utdlnNdpBJ6Ahwx7Bibl4MldiprGAyP+j6K6Epw2O06SMGjBjmeaZmCAayCPntmOirpyUm2pJBhdaHEorp+Ci1sGXkN+TSGarpHlyMQRSAxN9yR2JO0309Gri1AsDvz2DPz7/5wVbIxqP5L+mb2o9laRbnfhMLpCJ6AJJDJ54ESKPUUoqKRb0jDp0e3PhiSSzN1DbqWorgiDaiDdko5RC3Yo0nWdro6uPHL2PZTVlZNkSYxb716rbue3p1zB+Z3Pwaf5SLemY9UPDv2Wqqbz0Ig/UlRXgsVgIc2UhqERveZbQtlXb4eS7AG630PZV283e6KNZjzaIUOGcM4559S7/Pjx43nssce47rrr+Pbbb+nSpQtt27blm2++Ye/evdx8882heRVFYdeuXaSkpGCxWLjwwgtD0w6MO3vBBRdw1llnhZLfob766is+/vhjVDV4XB24il66dCn33XcfiqKQkJDAxRdfzNKlSyMS7dKlS/l//+//AZCRkcHZZ5/NsmXLQts6cHXbGFEfSbNmzWLWrFlkZWWF2kaOHMlll13WYKKtra3lzjvvpLa2FlVVSUpKYubMmSiKws0338zkyZPp06cPY8eOZc2aNZx33nkA3HbbbWG3rJubzaTSwVX/7TIFSHGYSXEc+JKIWRgRrEaVTpn13xbVdUiwGEnIqH+6CqQ7LaEz53i9kgTgUe2Q1PmYlvVihaSuBxsOS2Ye1QHJx7ZuRbeTbu14xOlW3UE7iwNXWuTVhqYZSDRmk5gQPEmM53FgCzjpZHHWexWk6+AxJUNKcr3L6rqJFHNbUvZ3mj38Lo9Zt5Bj3t+bv5l/J7NupY1l/9/t4SclukKikkyiLTkm2z4ag2Yi05R9xO0eOA7iHdex8leWNKq9KZo6Hu2gQYNwu91s3ryZWbNmcdlllwHBk68ePXrUe0G1d+9ebDZb2B3T+sadveqqq6L6Heobz/ZId2OPNt+R9sXRRJ1oHQ4HDocjou3ASPZHk56ezvvvv1/vtEOf0xoMBqZNmxZtSEIIcdIyJqbV25HQmJgWtxiiHY8WYOzYsbz55pssX7481Il2wIAB7Nq1ix9++IHTTz8dgJ9//pk+ffrUu736xp09PNGOHDmS119/nQceeABFUSgtLSU1NZXhw4fz4Ycfctppp+F2u5k3b169F4nDhg3jvffeY/LkyRQVFfHNN980uTBT1In2d7/7HbfffjsTJ04kKyuLvLw8Xn/9da6//nr27NkTmi+WV6BCCCGCUkb+NuwZLYBitJAy8rdxiyHa8WgBLrvsMs4991zGjx+PzRYsvpKUlMRLL73EU089xfTp0/H5fLRr146ZM2fWu476xp093L333sv06dO55JJLMBgMDBkyhAceeIBbb72Vv/zlL1x66aVA8HlvfWPUPvDAAzz00EOh+e6++266devW+J1ziKMOk3eonj17NrwyRWHjxo0NzhdLMkxeUGuNC1pvbBJX40hcjdfcw+TFo9exaLqor2g3bYqu+IAQQoj4cPY+SxLrcaDR3ery8vIoKCigf//+MQin9dB1KK72UFrpIS3JSlqCCYXYlok7mZn0OtSqYK1jNTkLjzk1rOORxV+JXp6LYjShJ+XgVWxHXlkjKAqYveXoFXnU1jgw2Vz4Dlm3AT/GmkK06jIMznS8NhfaIXVezFoNSkUuesCPkpKDx5B4yLoVKrVyCmuLsRutpFtcYSX9jPgxuPPRaypQna7guqMsRagoYK4rDvbEtjrRErLwKcd/uUC/6qWgrhCP30OG3RXXWshCxErUiTY3N5e77rqLTZs2oSgKq1atYsGCBXz33Xf13ic/3i3bXMirn65D14Nfan8Y35fB3dPljz4GzAE3tYvfpmbTEgAUs430K+6jLiH4vN9al0/pR48TcAff0bO064XzV5PwGBOPuM5oWdy5lHz4GFpd8D1la5eBOM65Aa8hAZUAbPuO4kX/Cs6sqKRe+Af0doPQdQWLv4LKBS/hzd0MgMGRTOrl91JnDT6jyvPt48kl/wiVcBzRbjBXdL8Us27FiB9t/eeULf4guG7VQNqY/8OX2bfBnuKKApaybRR/9AR6IPh+dMKA8zENugx/lK9UtUYeanh3wyyW5wbfebaZrEwZfhsuQ/3P/IQ4XkT9AttDDz3EOeecw8qVK0PvU40YMYIlS5bELLiWUur28tpn60NJVdfhlU/WUeaOfa3jk1LZnlCSBdC9tVR8/RYmvBhUHfeKOaEkC+DZswGtILIGa2OZlABV3/83lGQB6rb/BCW/AGCuLab8q0NeTNc1yr54FbOnDIBA3qZQkgUIuMupWb0Ag6oTUH38++cPQ0kWYPGe5eTVBgs5GNwFVBxIsgBagLIFMzH7yhuM26zVUPb5K6EkC1C96nMMlblHWar121uTG0qyALW+Ot5b/ym6Gn2pOyFao6gT7dq1a5k4cSKqqobeKXI6nVRVtc5OB01RVeOL6FDlD2gxrxl8stJryiPafAW/oAY8GDQv3tzIur++4r0N1jpuiBKow1sQOThBYH/NYK22MjTQQShWvxfqqlBVBX/R7ohlvfu2oGo+vLqXXRX7IqaX769AVd/vrNW54bBawPXG7avFX1EYubw7cp3Hk7J6akj/Ur4Xjx7bGuNCxFrUiTYtLS1iSLxt27aFqj2dSFITLdgs4XfVE2wmUpzNUz1HhFOTIusRW7sOJGC041ct2HqcHjHdnNOtyb3LAwY7tm5DItqNacFCDmpCKoox/Lmnak8ERyqapmNuG9kT39bjdPyKGati5bSs3hHTMxzBYe5UpwvU8DF1jUmZcKCQw9HiNjsx50RWxDEkHd+3WLP275tDDczpg7WZnscL0VKiTrQ33ngjkyZN4qOPPgrVqvzjH/8YVjrrRJFoNfHn3w4keX9iTU208udrBpJgiX6wcRG9QGJbks+9PpTUzDldSRh2BX7dgKaBpdc5WLucFpxZNZI4bDx62rFViQrbrq5gH3gxlna9AFAMJpLO/i2B5PYAeCxppI37UzC5AgZnKmlj7gqNsaund8F5+rhQwrR1HYS5x5nB5/qagSt6XkKXlA7B38lg4vr+V5FhDp5UeGwu0sf8H6o1WPDFmJRJyiV34FEarjrjw0zy6JswpQdPCBSzjdSLbsXnyGpgydYty5rNb/pchkkNnuT2SOvCJV1/BZp0QmwJ8Romb+zYsdTV1TU842Heffdd/vnPfzY435dfflnvaHHxFPV7tBAcsP29994jNzeX7OxsJkyYwOjRo2MZX6M113u0igI13uDtYqfdhM2k1tsRqrW+s9da44L6Y1NVMHlKwedFs6fgI/zugUnxodaWoqhGvNZUtGb88jXhRa0tw+qwU6U7OfTwURQw+6vAU4VuTcJncIQdBwZFx1RXiq4H0Gyp+A6rjxtQfVT4KjAbzDgVJ/ohvYoVRQk+k/W6wZZ8xCR7pM/SrNeh1JaByYbPkhLX98ePFldTKCpUahX4Aj6STcmoWuPrDR9vx/6B9tZm1KhRzJw5M6yesN/vr7fmcSzEc1ux1uBvsW7dOsxmM927d2f06NEMGDCA6dOns2XLFr799luGDRsWUZrxRKDrwVrINpMl9LOIHU0DjykVTPVP9+km2N+bN6J+bhP5MIMtk8Q0J1p9NYUNTrDv/yI87DgI6AoBS1q90yBYXzfVkB5aV/i6dTzGJDAmHVPcXsUK9v2PbuKcZGNF18BJEhho9s/5RPTdrh959+dPKakpJc2eytV9x3Jmh8jHIU0R62HyevTowcqVK3E4HBHD0N17773cd999bN26lczMTDIzM0lLS2PKlCnMmDGDmpoapkyZwscff8ycOXNITExk69atOJ1OZsyYgcvl4uOPP+brr7/m+eefB4Kj0L311lsAmEwmXn75ZZKTk7nlllsoKyvD4/HQt29fpk2bhtncPK/MNXjrePr06RQXH6yn+eCDD7Jr1y4mTJjA1q1beeqpp5olECGEENH7btePvLz8bYprStGB4ppSXl7+Nt/t+rHZt3VgmLxXXnklrP3QYfI+++wzHnnkkYhlDx0mDwg9ejzSKDgHhqGbPn06L774IomJiSxYsIDnnnuOFStWHDHGtWvXMmXKFObOnUvXrl35z3/+EzHPsmXLePnll3n99df57LPPeOutt3A6nRgMBp5++ulQwg4EAnz00UeN2ENH12Ci3b59e2jk+srKSr755hueeuopfvvb3/LMM8/w1VdfNVswQgghovPuz5/iDYT3yPYGvLz786fNvq1ohsmbP3/+Ea8Ax48fz6xZswDChsmrz6EJeNmyZYwfPx6A5OTkoz6qPO2000Kdc/v16xcauP1QX3/9NWPHjsXlCna8czgcWCwWNE3jjTfeYOzYsYwZM4YffvihWcsJN5hoA4EAJlPwft7q1atxuVx06tQJCA7UXllZ2WzBCCGEiE5JTWmj2puioWHyRowYwdKlSxk7diwej4dp06YxduxYxo4dy44dO444TF5D26pvaLsjsVgO9uswGAwEAoEofzuYPXs2P/30E2+//TazZ8/mN7/5DV5v871W1mCi7dq1K/Pnzwdg3rx5DBs2LDStoKAAp7P1PcQXQogTXZo9tVHtsZCfn4/BYGD06NHce++9lJaWhobJ+/TTT/n000/p3Dn4hsChw+Sdf/75Ua1/6NChfPLJJwBUVFTw5ZdfNinekSNH8umnn4Yeh7rdbrxeL1VVVaSkpJCQkEBVVVXoNndzabAz1N13380f/vAHHn74YVRV5Z133glNmzdvHqeddlqzBiROTrW42VeTS62vjqyEDFzGDNjfQ1dVwVq1C3/xXhSDCUNGR2rMB9+99Ste8usKKKktJdWaTJYtO6ymcFNYA5VQuotAdSnGRBdaSofgIPT7ualiT/U+AlqAnIQsUg3pB0soKjpF/kLyqguwm2y0cbTBph88W69RqtjrzqWirpIMRzo5thxM2sGzcqu3FK1kN1UFYE1ug8fqapZOeYoClrpiAiW7URQFNa09deb4jWF6rBQFSgLF5FbnY1ZNtEnIwUEznegrOsX+IvKqC7AaLbRNaINNb92dPK/uO5aXl78ddvvYbDBzdd+xcYuhqcPkNeS2227j3nvv5eKLL6ZNmzacdtppUY2BfiRDhgxh4sSJ3HDDDcFe/2YzM2fOZNy4cXz55ZdcfPHFZGZmMnDgQDweT8MrjFJUr/dUV1fzyy+/0LFjx7BfcseOHTgcjiPu2JYgw+QFtda4IDK2Wqp5cdU/2VkWfKaiKAp/HjaJDtbgIwp7xTYKPzxY19eY6CJt3F3UWLNB0fgy9xs+3jg/tL6Lu43igvajUfXGvRpweFxm6vAse5/qNYtCbUnDLsPQ71J8mkqVXs5TP/yDktpgSUaTwcR9I+4gw5iFosCOmu387YdXQom3a2pHJg24Hptux6PW8N+Nn/DjvtWhdf+mzzjOzBqOFgBrXSElHz6KVhN8NKOYbaRf9SB19pxG/U71sdbmUfz+X9A9NQCotgTSrnwwVKM5WvE+xvJ9uTy++AX8WrAkY1aCiz8OuYUEwmteH0tcezy7eHLJP9D2VwLrkNSG2wfehJ1j/1KvT3O/3hOPXsctyefzoWkaFouF6upqrr76au69916GDx/e0qE1SlQFKxISEujdu3fEmUTnzp1bVZIVx6e97txQkoXgc5n/rP0Yv+rFYvBTsezTsLq+/soivHs3AFAeKGfWpgVh65u7dRFl/rImx2Wo3BeWZAEqfvgUU/U+FAU2lW4NJVkAX8DHvG1foqg6XsXDWz9/FDZAwLbSX8h1B+sR59UWhCVZgA83zKU0UIyqKnh3rAglWQjWf65du6jJZScNBoXa9V+HkiyAVluNZ+uyJq87lnQ1wEeb5oaSLEB+dRE7Kn5p8roDqo+3184KJVmAXRX72OuOLKHZ2pzZYQgvXfoY7/36H7x06WMnVJKFYAfcq6++mrFjx3LFFVdw/vnnH3dJFo5hmDwhmluNrzairbimFL/uQ9UUApXFEdMDVaUYDCq13rp6R7up8deR1sRCXrqnnrrDuobuqUVJUCiqp9NJXnUhAQL4dX9YEg7Ftf93rfHVREzzBnzU+T0oFgVvWV7EdF/JXkyqjtaE90sVRcFfEplAfKW5WFUl7kUvouUnQJG7JKK9rLYCJblp77n7dV+9n1W11w2t++7xCS8tLY2PP/64pcNosqhLMAoRKzkJWRFj/Z7TYRg2xY7XkICj1xkRy1janUIgoJFqSSH9sM4fSdZEXNamdwhRk7JRbeG39IzJmShJmWiaTm9XZK3jkR2HY9BN2BQ7Z7UfGjZNURSyE4J3gLITMrEYw6tfdUhuS6ollUBAw9YtfFkAR59R+Js4kI3fr2HvfU5Eu73ncPz+1lshwoKFkZ1GRLR3Se3Y5OfWVsXO2R2GhbUpKLRxnnh13EXLkEQrWpzL5OKPw27GZU/FqBo5t9MZ/KrTOeiaQiCgYe52OomDL0YxWTAkJJN2wc3o6d0AsOg2/m/I7znV1R1FUeiR3oU/nX4L1mboyFJndeG67C4s2V1BUbG270X6JXdQowYrObWxteGWgdeQZHFiMZi5rOcF9Hf1CV5hawoXdj43mHhVA5mOdP50+i2kG4Pv76WpLu4adjMdktugKAp9M0/h96dNwBwIdhLR0ruR8qubUG0JKGYbSWf/FnIiByk4JtmnknzONSgWO6o1geTRN6BlRA5S0Jpoms6QzNO4tPtozAYTKdYkbhv8O3IsTX9mrWswqsMZnNf5LIyqkXR7Kv93+u/JMMtjMdE8GlXr+HggnaGCWmtcUH9sigJexUNA92NTHIePTofBABZPKagG6oxJEbdPNdWPFw9mLMdUH/dIcQFYqcXgdxMwOanTw69CFUXBo9Sg6To2xR4RN6pOnV6DUTFi1CJHf/IZ6vDoHhyKAyUQHreqKpj81dhtJip8lmY9rlVVweSrAkXBZ0w4pnW3xDGmqFBLDSoqFt3arPXHFVWnVq/FoBgw65aYlF09nmodi+Yjz2hFq6DrYNItmKj/Cy4QgBrj/tvB9dzhVDUj1hgdznXYwGirt5axruuYddv+/9ezsKZgxVHvsgCmgBUT1nqnaZqOR3WQmBhZg7mpNE3HY9jfubGVPpetj66BleArUs0dta4pwXXrzb9ucXKTW8dCCCFEDEmiFUIIIWJIEq0QQggRQ5JoRYSWKlygKE3btsVy7M9oY/k7q6rC0eqit+ZCEUKIppPOUCLEpNWhlGzHu2cDxrS2GHJ64jGlxGXbVm8Jvj3rCFQVY+nQl0BKR/xHGgX+MLUUs7l8N9vLdtMpuR09ktvjUFxRLWvAj7FiN56dqylPTMXa5lTqLNEt25CA4mNf7T7WFW0i3Z5Gz9RuJCrJoek1VLG1fAe7K/bRM70rHRztMR+hY5QQ4vgliVYAoCoQ2PQVFd+/F2ozZXQkcczdeNXmrfd6OIuvjNIPHyNQHay0VPXjbFIvvh2l7aCGX7FQ3Xy46X/8sHdVqKl/Vi9u6nM5aqDhVyYMBRso/vSZg6uzJ5J61UN4zOnH9LscoCgK68o38MpPb4fa0mwp3DPsNhJIxKvU8cqqt9laugOABdu/5pLuo7mw/a9AkytcIU4kcutYAGDyllG5NLzUma/wFyiLfb1XvXhXKMkeUPHtO5i1yDKFh8v3lIQlWYDV+RvIrSlqcFkzHiq/+29Ym1ZTiVawI4qoj86j1PLfdZ+FtZXUlrG3OljruKCuMJRkD5i3dRGVgYomb1sI0bpIohVBWgA9UE99P62JNf+icOiAAaE2bx2R1R8i+Y4Qn19reNBnRQ+g+eoit+1v+oDPmq7h8UcOsxXYH1egnvg0XUPTox+sWghxfJBEKwAIWFNwnHpmWJtqS0BJbhPzbRvS2qMYw8ePdQ4Zg9fQ8C3rbHs6HZLbhrc5M8m2N/yc1Wdw4Bx8aXijasSY2bnhoBtgU+xc1G1UWJvZYCInIQuADLuLZGv48G4Ds/uQaExu8raFEK2LPKMVAPh1A7ahl2NMzaFm4/eYM7tgP+1C6kzJMd+2x55J+lUPUr38M/zl+Tj6/QpDh9PwRlGexxhIZOKAX/P17h/ZULiVHumdGdlhKBa94bg1TcfUaSgp51twr/ocgzONhCFj8Diym1waSNfgzDan47Qk8NUvS8hyuLi467mhgeHtegJ/HvYHFu78ji0lOxjadgCnZw9C1Zo45JAQotWRWsdN1FprCh9rXKqqYNA8aKqJQIw65RwpNqOioaLhw9joOrMGEwT0GkyKHV/kneijUhQw4iMx2UlJWeSt5KZQFIWA6sOAAT0QuT8VFQL4MWI64nF7oh1jsdZa4wKpdXyykitaEUbTdDTM9dYTjjW/rnKsTzOCj3ntNDLHAsEaxT5MqEYT0LyJVtd11IDxiBfIugYqRjSprivECUue0QohhBAxJIlWCCGEiCFJtEIIIUQMSaIVcaMoCibDsT+LNBrAYKi/g5aqBtcdi7rBqqqgGqUm8clANR75GBPiWElnKBEXFn8Fvh3LcW9ZRqDDqVh7nEFdlGUOjboHtXAz7lWfo9oTcQy4AE9ie3Q9+IVo8ZXh27oM944VWDr1x9p9OHWm1GaJu0ovZ9nelawt3MTA7L4MzOyHA+kheqLxKR42V2zlq18Wk+FI59yOZ+AyZnGCvZQhWogkWhFzJsWPe/E71G5eBoAndyvGTctIHn8/niiKUij5GyiZ/Vzo55otP+K6ehp1jraYdA9VC1/Fs2fD/nVvw7x9Fc5L78ar2JoUt1et46Xl/2J3RbAM5bbSX9hQvIWb+1yLQYtuwAPR+qmqwsqiNby15kMANhVvZ9neVTx45h9JUdNaODpxIpBbxyLm1JqSUJI9wF+Wh16R1+CyZsVP9bJPwxu1AN4961EUBdVdFEqyB3jzt0NVQZPjLqotDiXZA9YWbKLUW9bkdYvWo45aPt38RVibJ+BlT1Xs63yLk4MkWhF7igLUV6yh4cNPBzBE3nhRDMbg1CMM9KooTT+01SOs40jt4vikoGBUIityyecsmoscSSLmArY0EvqdG9Zmzu6Cnpjd4LI+3Uji6ZeFtSlGM6a2vdB10BwZ2LoPCZtu6dgXLSGzyXGnW9M4Jb1rWNuwtgNJiUNZShE/Zt3CFb0uDmtzmO20d7Y9whJCNI6UYGyi1lrurbXFZQ5Uo+VtxLNzNbZ2PVHa9I56UHkDfozlv1C7aQmqPRFr1yF4HG1CHVXM/kq03PV4dq3D0u4UDG374DEmNTrG+vaZm2o2lGxmS+l2ert60D25KzYcjV53U7S2z/KAEykuv+Jjd81ulueuxmVPY0BmH1INaY0uBXqssUkJxhObJNomOpG+bOLBYFBJTXUcU2wGg4qu6/V+vooCqqqiadoxfzkeaZ8dWHcg0AJ1KWm9n+WJGNfRjrHmIIn25CS9jkVcNSVZHW1ZXW/auo8mlusWrYt8ziIW5BmtEEIIEUOSaIUQQogYkkQrhBBCxFBcntGWlZVxzz33sHv3bsxmMx06dOCRRx4hNTW8TN6MGTN45513yMjIAOC0005j6tSp8QjxuKKoUKfXYlJNqIHGfYSKAiatFgCfamv2XpVHY1A1jP4aNG/9VZVMeFE0H36jA+2wR2WqqlBHLQZUDJo5DtE2D4MBPIFKTKoZXbPGddu66ser+7AqVnRN6vcK0VLikmgVReH3v/89Q4cOBeCJJ57g6aefZvr06RHzjhs3jilTpsQjrOOSmyq++uV7vtv9I1kJLiacOpYcc5uoEqZR96DsXkXFko8ASBx+OXq7AfgVS4yjBqu3hJoVn1G5bQXunK4kDP81HkcOug6KomMp207FN2/jryohod+5WE4ZiceYCIBHqWVFwSrmbV2E3WTj16eOoYujM4oeWWSgNfHopXy1fQlf7/2JdFsKv+l1EW3sXUI1mmNFURTyfbm8v2E2eypzGd52IL/qeDYOEmO6XSFE/eJy6zg5OTmUZAH69+9Pbm5uPDZ9YlE15m7/H/O3fUW118220l/46+IXKQ2URLd44RZKF8zEX1mEv7KI0gUzUQu3xDhoMOGh4ouZuNd9g1bnpnbHGko+mo7ZVwGAxZ1H0QfT8RbsRKuppHLpLDzr/oeqBJPGmuJ1vLP2E8rrKsmtKuDZH14l19Nw+caWpBp0vtj5LbN3fEuV183Oir389YfXKPPGvqxfuVbKXxe/wKbibbi9Nfxvx3d8sGk2uio9aoVoCXF/RqtpGu+++y6jRo2qd/rcuXO59NJLufHGG1m1alWco2vd3Jqbb3cfVjNY85Pnbriur9GoUrP+64j2mvVfYzTG9jBQ3SV4c7eGtWm11egV+QAESvaAHp4Eqlf9D5O/Gr/q5Ysd30asc1PJVpQjlF9sDTyBcr7c/WNYW0DX2FsZ+xOEfHch3oAvrG157hqqA5Ux37YQIlLc36P9y1/+gt1u55prromYNmHCBCZNmoTJZGLx4sXceuutzJs3j5SU6CoIAaSlNTwaTHOL18vmhho/CWYHVZ7qsPYEi73eGA5vK07JovaweUxJGaSkxLbSkVdxBAf61Pxh7WaHgySXk+riyO0bHEnYEx1YzVbS7MnkHTZIQLItifT05v+sm+uzLKusJdHipKQ2fAACm8l6TNtozDL7/PaINpvJitNhx+Vo3mO1tRZaaK1xQeuOTcRGXBPtE088wa5du5g5cyZqPQXlXS5X6P8jRowgOzubrVu3MmTIkIh5j+RErgylKCau7XM5L634V6itc0oHMswZETHUF5e1xwiUNV+ie+uC6zNZsZxyZszjVxUnSSOuoOK7/4babD1Ox2NxUV1UhSW5Haa0tvhK9oamJ4+8jvIaBd3tYWz389lYuJXA/qveJGsi3ZI6NXvczflZqqqNa069mOdW/CfU1i4hk7YJbRq9jcbGlW5Kp2d6VzYVbwu1/ab3ONQ6C0U1zbfPTsTKULEmlaFOTnErwfjss8+ycuVKXnnlFWy2+scJLSgoIDMzWAx+48aNXH/99cyZMycsATfkRE60AJoSIN+bz97KXBItTto722LXI6/s6otLUcBSW0igaCe6rmPM6IzHlhGXnsdG3YOhfDf+0n1YUjLxJ7XDe8hYtBZfOXrxTgJ1VRjTO+JztkFjf2cnRafYX8Suyr1YDGY6JLbFSXKzx9jcn6Wi+Cio3cuuyn04zQ46JrbHegzjmx5LXLW42ePeS1ldBW2dOWRbslD15j2vbq0JrbXGBZJoT1ZxSbRbt27lkksuoWPHjlitwVcc2rZty4svvsjNN9/M5MmT6dOnD1OmTGH9+vWoqorJZGLy5MmcffbZjdrWiZ5oo9Va44LWG5vE1TgSV+NJoj05xeXWcbdu3di8eXO901599dXQ/5944ol4hCOEEELEjVSGEkIIIWJIEq0QQggRQ5JohRBCiBiS8WiPQyZ8qJ4KMFrwGhOJU8fxJrNp1Sh15XhLk1BVZ0Q9YyGEOBFJoj3OWL3FVH75Bp49G1BtCaSM/j3+7D4HX4VppezVv1D6xev4ivdgcKaR9qsb8WScKslWCHHCk1vHxxGT4qfq27fx7NkABMsYlsx+DrM7v4UjOzq7VkHJvH/gK94DQKCqhKLPnsNaI/WuhRAnPkm0xxGDt4q6HYfXf9YJlLfuRKtXF+MvDy+hqPu9BMpad9xCCNEcJNEeR3SjFWNSRkS7Ym/dw58pFgeKKXIsVtWe1ALRCCFEfEmiPY54VRvJ5/0e1IPPY+09h6MntWnBqBpWZ8sidVT4IBLOgReiJeW0UERCCBE/0hnqOKLr4E3phuua6QTK81GtCWiJOfjU+mtHtxaaBlqHoWRc3YZAeQFGZyr+xLZ4aN1xCyFEc5BEe5zRUaizZkJWZkuH0ih+TPidncDZiaRWXItWCCGam9w6FkIIIWJIEq0QQggRQ5JohRBCiBiSRCuEEELEkHSGOkaaDiXVHordXhJtJsyq0tIhAaAqGqa6Emp378FiSsJrSqQ1lEJWFAWzrxzdXYJXSUNVktBaQVyxZtHcUF2EYrbhs6UT0Ft3qUwhRPOTRHsM6vwaH32znS+XB0sKdsxO5M6r+pNka9ndacCPsn0pxYv+BZof1Z5I2ti7qHN2bNG4FAUsZVsp+fQZNE8NisFEynk3Q7tBaCfwTRVbbT4lnz5NoLIYFJXEYeMxnvor/IqlpUMTQsTRifstF0M78ypDSRbgl7xKPl+2C6WF96bRXUDZwtdB8wOg1VRSNu8fWPSaFo3L7K+iZM7zaJ5gHHrAR+mCmZhri1o0rlgy4aPim38HkyyArlG55EMMFXtbNjAhRNxJom0kVVXYnlsZ0b5qSxFef8veC9WqSiLa/BUF4KlugWgO0msr0WoO22e6huYubZmA4kAN1OLZszGiPVBV3ALRCCFakiTaRtI0nU7ZkbWF+3VNx2Ro2ee0akJqRJsx0QXmhBaI5iDF5kQ9vB6zoqI6UlomoDjQDDYsbbpHtBucaS0QjRCiJUmiPQadsxM5s//B+sJtXAlccHoHaOHOPX5HJskjr+PAPWzVYiflolvxGuwtGpfXmEjqRbcdHFhANZJy3u/x2lwtGlcs+TCROPI6DI7k/S0KziFjCCS2bcmwhBAtQNH11tAntfmUlFSjxaE7a0CH4so6FFUhyW7CYmgd5yyqomGuLcYYqMFnTsZrTm4lvY7B7C1Dry7FmpxKtZqEpreOfXaAKwalIS2BKvSqYK9jv91F4Bj6H8YiruYgcTXekWJzuZwtEI2IF+l1fIwMCmQmWVvdH7Wmq9RZM3C5nFQXVbX4VfYBug4eUwqkpJDkclLZivZZLHkMTkiWL1EhTmat65JCCCGEOMFIohVCCCFiSBKtEEIIEUOSaIUQQogYks5Q4qSmquD2FZFXnU9ujYVMezYGou+8ZNbcUL4PtABKcjYeY3LsghVCHJck0YqTWpFnD48vfZVafx0AfdK6clPfKzEpDRfTsPjKqZz/PN78HQDB2tJX3EedNSumMQshji9y61ictFSDj/c2zAslWYC1JdvYVbnnKEsdFMjbHEqyEKwtXbNyPga1lbxTJYRoFSTRipOWN1DDrqr8iPbi2vIGl1VVBX/x7sh15m1D1XzNEZ4Q4gQhiVactCyGRIZk9opob+ds+NavpumY250S0W7vOYKAKsPgCSEOkkQrTlqBgMJFXc+hb3qw+L/ZYOLaXpfQxtEuquX1tC4kjrgSDMGuDraewzD1GBGXEqBCiOOHdIYSJzWb4mLSgGup9JRhNZmxqKlogeiW9ak21N4Xkd5tGOgBAtZUPLohtgELIY47kmjFSU8PWHAas3ClN75utaYreMz7hyeUC1khRD3k1rEQQggRQ5JohRBCiBiSRCuEEELEkCRaIYQQIoYk0QohhBAxJIlWCCGEiCFJtEIIIUQMSaIVQgghYkgSrRBCCBFDkmiFEEKIGJJEK4QQQsSQJFohhBAihiTRCiGEEDEkiVYIIYSIIUm0QgghRAxJoj0BKUpLRyCEEOKAuAz8XlZWxj333MPu3bsxm8106NCBRx55hNTU1LD5AoEAjz76KN999x2KojBx4kSuvPLKeIR4wrD4ygjsXUfRsl2Y2/dFz+iKT7W3dFhCCHHSissVraIo/P73v+fzzz9n9uzZtGvXjqeffjpivtmzZ7N7926++OIL3nvvPWbMmMHevXvjEeIJwRKopmL2M5T973WqVi+k5LNn8K//ElXRWzo0IYQ4acUl0SYnJzN06NDQz/379yc3Nzdivnnz5nHllVeiqiqpqamMHj2aBQsWxCPEE4Jevg9f8Z6wtsofP8XkLW+ZgIQQQsTn1vGhNE3j3XffZdSoURHT8vLyyMnJCf2cnZ1Nfn5+o9aflpbQ5Bgby+Vyxn2b9XGX1dOoaVjNKonprSPGA1rLPjucxNU4ElfjtebYRGzEPdH+5S9/wW63c80118Rk/SUl1Wha/G6VulxOioqq4ra9o7E4M1HtiWg1laE2x6ln4cZJRSuJEVrXPjuUxNU4ElfjHSk2Sb4ntrj2On7iiSfYtWsXf//731HVyE1nZ2eH3VLOy8sjKysrniEe1zzGZNKuuJ+EfqMxZ3Yk6exrsA0Zj1+XzuVCCNFS4nZF++yzz7Ju3TpeeeUVzGZzvfNccMEFfPDBB5x33nmUl5ezcOFC3n777XiFeEKos2ZiGHYNOYlmSit8eHTpCCWEEC0pLpc6W7duZebMmRQWFjJhwgTGjh3LbbfdBsDNN9/M2rVrARg7dixt27blvPPO46qrruK2226jXbt28QjxhKJpoJos6JJkhRCixcXlirZbt25s3ry53mmvvvpq6P8Gg4Fp06bFIyQhhBAiLuThnRBCCBFDkmiFEEKIGJJEK4QQQsSQJFohhBAihiTRCiGEEDEkiVYIIYSIIUm0QgghRAzFvdZxrKlq/Ec9b4ltRqO1xgWtNzaJq3EkrsZrzbGJ2FB0KR8khBBCxIzcOhZCCCFiSBKtEEIIEUOSaIUQQogYkkQrhBBCxJAkWiGEECKGJNEKIYQQMSSJVgghhIghSbRCCCFEDEmiFUIIIWLohCvBGEsvvPACM2bMYPbs2XTv3j1s2owZM3jnnXfIyMgA4LTTTmPq1KkxjWfUqFGYzWYsFgsAd999N2eeeWbYPIFAgEcffZTvvvsORVGYOHEiV155ZUzjija2lthnHo+H6dOns3TpUiwWC/379+cvf/lL2Dwtsc+iiSve+2vv3r3cdtttoZ+rqqqorq7mxx9/DJuvJfZXtLG1xDH21Vdf8dxzz6HrOpqmcccdd3DeeeeFzdNSf5eiZUiijdL69etZvXo1OTk5R5xn3LhxTJkyJY5RwfPPPx+R9A81e/Zsdu/ezRdffEF5eTnjxo1j2LBhtG3btsVjg/jvs6eeegqLxcLnn3+OoigUFxdHzNMS+yyauCC++6tt27Z8+umnoZ8fe+wxAoFAxHwtsb+ijQ3iu890Xeeee+7h7bffpnv37mzatImrr76a0aNHo6oHbyC25N+liD+5dRwFr9fLI488wtSpU1GU46sg+Lx587jyyitRVZXU1FRGjx7NggULWjqsFuF2u/nkk0+48847Q59jenp6xHzx3mfRxtWSvF4vs2fP5vLLL4+Y1tLH2NFiawmqqlJVVQUEr7QzMjLCkiy0/D4T8SVXtFF47rnnGDNmDO3atTvqfHPnzuX777/H5XJxxx13MGDAgJjHdvfdd6PrOgMHDuSuu+4iMTExbHpeXl7YVXh2djb5+fkxjyua2CC++2zPnj0kJyfzwgsvsGzZMhwOB3feeSeDBg0Kmy/e+yzauKBljjGARYsWkZmZyamnnhoxrSWPsYZig/juM0VR+Pvf/86tt96K3W7H7Xbz8ssvR8zX0vtMxJdc0TZg1apVrF27lt/85jdHnW/ChAl8+eWXzJ49m5tuuolbb72VsrKymMb29ttv89lnn/HRRx+h6zqPPPJITLfXGNHEFu995vf72bNnD7169eLjjz/m7rvv5o477qC6ujpm22zOuFriGDvgo48+ajVXjIc7WmwtcYy9/PLLvPTSS3z11Vf84x//4I9//CNutztm2xStnyTaBixfvpwdO3Zw7rnnMmrUKPLz87npppv4/vvvw+ZzuVyYTCYARowYQXZ2Nlu3bo1pbNnZ2QCYzWZ+85vfsHLlynrnyc3NDf2cl5dHVlZWTOOKNrZ477OcnByMRiOXXHIJAP369SMlJYWdO3dGxB7PfRZtXC1xjAEUFBSwfPlyLr300nqnt9QxFk1s8d5nGzdupLCwkIEDBwIwcOBAbDYb27dvD5uvJfeZiD9JtA2YOHEi33//PYsWLWLRokVkZWXx+uuvc8YZZ4TNV1BQEPr/xo0b2bdvH506dYpZXDU1NaHnQLquM2/ePE455ZSI+S644AI++OADNE2jtLSUhQsXcv7558csrsbEFu99lpqaytChQ1m8eDEAO3fupKSkhA4dOoTNF+99Fm1c8d5fB8yaNYuzzz6blJSUeqe3xDEWbWzx3mdZWVnk5+ezY8cOALZv305xcTHt27cPm68l95mIP3lG2wQ333wzkydPpk+fPjzzzDOsX78eVVUxmUw8+eSTuFyumG27pKSEO+64g0AggKZpdOnSJfTawqFxjR07ljVr1oReL7jtttsafNYcr9jivc8Apk2bxn333ccTTzyB0WjkySefJDExscX3WTRxtcT+gmAyu//++8PaWnp/RRtbvPeZy+Xi4YcfDuvY9vjjj5OcnNxq9pmIP0XXdb2lgxBCCCFOVHLrWAghhIghSbRCCCFEDEmiFUIIIWJIEq0QQggRQ5JohRBCiBiSRCtOCqNGjWLJkiUtHUaYa6+9lg8++KClwxBCxJgkWhF3K1asYMKECQwcOJAhQ4YwYcIEfv7557htf8aMGdx9991x296xbtPr9TJjxgzOO+88+vfvz6hRo7j33nvZu3dvjKIUQsSCFKwQcVVdXc2kSZN4+OGHufDCC/H5fKxYsQKz2dzSobU6kydPpqCggKeffppevXpRW1vLZ599xtKlS2XsUiGOI3JFK+LqQP3eSy65BIPBgNVq5YwzzqBnz54AfPjhh1x44YUMHjyYm266iX379oWW7dGjB2+99RbnnnsuQ4cO5YknnkDTNAB2797Nddddx9ChQxk6dCh/+tOfqKysbHR8q1evZsKECQwaNIgxY8awbNmy0LRrr72Wv//970yYMIEBAwZw4403UlpaGpr+ySefMHLkSIYOHcqLL74Yul397bff8vLLLzN//nwGDBjAmDFjQsvs27ev3vUtWbKEJUuW8NJLL9G3b1+MRiNOp5Pf/va3oSR77bXX8uyzz4aWnzRpEmVlZfzpT3/itNNO4/LLL5erXyFaAUm0Iq46deqEwWBgypQpfPPNN1RUVISmLVy4kJdffpkXXniBpUuXMnDgQP70pz+FLf+///2Pjz76iFmzZrFo0SI++ugjIFhT+ZZbbuG7775j/vz55OfnM2PGjEbFVlBQwC233MIf/vAHfvzxR6ZMmcLkyZPDkumcOXN4/PHHWbp0KT6fjzfeeAOAbdu2MW3aNJ566im+++47qqurQ3V2zzrrLG655RYuvPBCVq1axWeffdbg+pYsWULfvn1DgzMcybx583jyySf59ttv2b17NxMmTODyyy/nxx9/pEuXLrz44ouN2gdCiOYniVbEVUJCAu+88w6KovDggw8ybNgwJk2aRHFxMf/973+ZOHEiXbp0wWg0MmnSpFAh+ANuvvlmkpOTycnJ4brrrmPOnDkAdOjQgREjRmA2m0lNTeWGG25g+fLljYrt008/5ayzzuLss89GVVVGjBhB7969+eabb0LzjB8/nk6dOmG1WrngggvYuHEjAAsWLGDkyJEMGjQIs9nM5MmTQ7Vuj+ZI6ysvL4+qJu/48eNp3749TqeTs846i3bt2jF8+HCMRiMXXHABGzZsaNQ+EEI0P3lGK+KuS5cu/PWvfwWCo5v8+c9/Zvr06eTm5jJ9+nSeeOKJ0Ly6rlNQUECbNm0Awq7w2rRpQ2FhIRAcyODRRx9lxYoVuN1udF2vd6D5o8nNzWXBggV89dVXoTa/38/QoUNDPx+a/Gw2GzU1NQAUFhaGDXNms9lITk5ucJtHWl9ycjK//PJLg8unp6eH/m+xWMJ+tlqtofUJIVqOJFrRorp06cL48eN57733yM7OZtKkSWHPMA+Xl5dHt27dgGBizMjIAOBvf/sbiqLw2WefkZKSwsKFC+sdbP5osrOzGTt2LI8++mijf4+MjIyw8WPr6uooLy8P/RzN1e2hhg8fzltvvUV+fr6MUyrEcU5uHYu42r59O2+88Qb5+flAMHHOmTOHfv36MWHCBF555ZXQwNxVVVXMnz8/bPnXX3+diooK8vLyeOutt7jooosAcLvd2O12EhMTKSgo4LXXXjtqHLqu4/F4Qv+8Xi9jxozhq6++4rvvviMQCODxeFi2bFko1qM5//zzWbRoEStXrsTr9fL8889z6MBYaWlp7Nu3L9R5qyHDhw9n+PDh3Hbbbaxbtw6/3091dTXvvvsuH374YVTrEEK0DnJFK+IqISGBNWvW8Oabb1JVVYXT6WTkyJHcc889JCQk4Ha7ueuuu9i3bx9Op5Phw4dz4YUXhpY/99xzGT9+PNXV1Vx22WVcccUVANx+++1MmTKFQYMG0b59e8aOHcs///nPI8YxZ86c0PNdgMzMTL799lteeuklnnrqKf70pz+hqip9+/bl4YcfbvD36tatGw8++CB33XUXtbW1XHfddaSmpoZeW7rgggv47LPPGDp0KG3btmXWrFkNrvP5559n5syZ/PGPf6SoqIiUlJRQ8hVCHD9kPFpx3OjRowdffPEFHTp0aOlQGuR2uxk8eDCff/65DOgtxElObh0L0UwWLVpEbW0tNTU1PPHEE3Tv3p22bdu2dFhCiBYmiVaIZvLll19y5plncuaZZ7Jr1y6eeeaZRneCEkKceOTWsRBCCBFDckUrhBBCxJAkWiGEECKGJNEKIYQQMSSJVgghhIghSbRCCCFEDEmiFUIIIWLo/wNnajVzIH+gLgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 474.35x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "sns.relplot(data=iris, x=\"PetalLengthCm\", y=\"PetalWidthCm\", hue=\"Species\")\n",
    "sns.relplot(data=iris, x=\"SepalLengthCm\", y=\"SepalWidthCm\", hue=\"Species\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f97ef942eb0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 474.35x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "irisv = iris[iris[\"Species\"] !=  \"Iris-setosa\"]\n",
    "sns.relplot(data=irisv, x=\"SepalLengthCm\", y=\"SepalWidthCm\", hue=\"Species\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f97f2ad3550>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 834.35x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=iris.drop(columns=[\"Id\"]), hue=\"Species\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pobieranie z HuggingFace &#x1F917; Datasets\n",
    " - Szukamy na https://huggingface.co/datasets/\n",
    " - Klikamy w \"</> Use in datasets library\" i kopiujemy kod\n",
    " - Instalujemy bibliotekę datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting datasets\n",
      "  Downloading datasets-2.10.1-py3-none-any.whl (469 kB)\n",
      "\u001b[K     |████████████████████████████████| 469 kB 683 kB/s eta 0:00:01\n",
      "\u001b[?25hCollecting responses<0.19\n",
      "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
      "Collecting xxhash\n",
      "  Downloading xxhash-3.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
      "\u001b[K     |████████████████████████████████| 212 kB 866 kB/s eta 0:00:01\n",
      "\u001b[?25hCollecting pyarrow>=6.0.0\n",
      "  Downloading pyarrow-11.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (34.9 MB)\n",
      "\u001b[K     |████████████████████████████████| 34.9 MB 956 kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /home/tomek/miniconda3/lib/python3.9/site-packages (from datasets) (1.24.2)\n",
      "Requirement already satisfied: requests>=2.19.0 in /home/tomek/miniconda3/lib/python3.9/site-packages (from datasets) (2.27.1)\n",
      "Collecting aiohttp\n",
      "  Downloading aiohttp-3.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
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      "  Using cached async_timeout-4.0.2-py3-none-any.whl (5.8 kB)\n",
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      "  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
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      "Requirement already satisfied: six>=1.5 in /home/tomek/miniconda3/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
      "Installing collected packages: multidict, frozenlist, yarl, async-timeout, aiosignal, pyyaml, fsspec, filelock, dill, aiohttp, xxhash, responses, pyarrow, multiprocess, huggingface-hub, datasets\n",
      "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.10.1 dill-0.3.6 filelock-3.9.1 frozenlist-1.3.3 fsspec-2023.3.0 huggingface-hub-0.13.2 multidict-6.0.4 multiprocess-0.70.14 pyarrow-11.0.0 pyyaml-6.0 responses-0.18.0 xxhash-3.2.0 yarl-1.8.2\n"
     ]
    }
   ],
   "source": [
    "#Instalujemy bibliotekę datasets\n",
    "!python -m pip install datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset csv (/home/tomek/.cache/huggingface/datasets/scikit-learn___csv/scikit-learn--iris-4e13227f45447466/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317)\n",
      "100%|██████████| 1/1 [00:00<00:00, 268.64it/s]\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "iris_dataset = load_dataset(\"scikit-learn/iris\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],\n",
       "        num_rows: 150\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'],\n",
       "    num_rows: 150\n",
       "})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dataset[\"train\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Id': 1,\n",
       " 'SepalLengthCm': 5.1,\n",
       " 'SepalWidthCm': 3.5,\n",
       " 'PetalLengthCm': 1.4,\n",
       " 'PetalWidthCm': 0.2,\n",
       " 'Species': 'Iris-setosa'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dataset[\"train\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>2</td>\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>3</td>\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</td>\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</td>\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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0      1            5.1           3.5            1.4           0.2   \n",
       "1      2            4.9           3.0            1.4           0.2   \n",
       "2      3            4.7           3.2            1.3           0.2   \n",
       "3      4            4.6           3.1            1.5           0.2   \n",
       "4      5            5.0           3.6            1.4           0.2   \n",
       "..   ...            ...           ...            ...           ...   \n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "0       Iris-setosa  \n",
       "1       Iris-setosa  \n",
       "2       Iris-setosa  \n",
       "3       Iris-setosa  \n",
       "4       Iris-setosa  \n",
       "..              ...  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(iris_dataset[\"train\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Podział danych\n",
    " - ### Zbiór trenujący (\"training set\")\n",
    "  - Służy do dopasowania parametrów modelu (np. wag w sieci neuronowej).\n",
    "  - Podczas trenowania algorytm minimalizuje funkcję kosztu obliczoną na zbiorze treningowym \n",
    " - ### Zbiór walidujący/walidacyjny (\"validation set\" aka. \"dev set\")\n",
    "  - Służy do porównania modeli powstałych przy użyciu różnych hiperparametrów (np. architektura sieci, ilość iteracji trenowania)\n",
    "  - Pomaga uniknąć przetrenowania (overfitting) modelu na zbiorze trenującym poprzez zastosowanie tzw. early stopping\n",
    " - ### Zbiór testujący (\"test set\")\n",
    "  - Służy do ewaluacji finalnego modelu wybranego/wytrenowanego za pomocą zbiorów trenującego i walidującego"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Podział danych\n",
    "- Zbiory trenujący, walidacyjny i testowy powinny być niezależne, ale pochodzić z tego samego rozkładu\n",
    "- W przypadku klasyfikacji, rozkład klas w zbiorach powinien być zbliżony\n",
    "- Bardzo istotne jest to, żeby zbiory walidujący i testujący dobrze odzwierciedlały nasze cele biznesowe i rzeczywiste dane, na których będzie działał nasz model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Metody podziału:\n",
    "- Skorzystać z gotowego podziału danych :)\n",
    "- Jeśli dzielimy zbiór sami:\n",
    "  - \"Klasyczne\" podejście: proporcja Train:Dev:Test 6:2:2 lub 8:1:1\n",
    "  - Uczenie głębokie: \n",
    "    - metody \"głębokie\" mają bardzo duże zapotrzebowanie na dane, zbiory rzędu > 1 000 000 przykładów\n",
    "    - Załóżmy, że cały zbiór ma 1 000 000 przykładów\n",
    "    - wielkości zbiorów dev i test ustalamy bezwzględnie, np. na 1000 albo 10 000 przykładów\n",
    "    - 10 000 przykładów to (wystarczająco) dużo, choć stanowi jedynie 1% z całego zbioru\n",
    "    - szkoda \"marnować\" dodatkowe 180 000 przykładów na zbiory testujące i walidacyjne, lepiej mieć większy zbiór trenujący \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Przykładowy podział z pomocą standardowych narzędzi Bash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-03-15 11:16:36--  https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\n",
      "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
      "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
      "HTTP request sent, awaiting response... 416 Requested Range Not Satisfiable\n",
      "\n",
      "    The file is already fully retrieved; nothing to do.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Pobierzmy plik ze zbiorem z repozytorium\n",
    "!cd IUM_02; wget -c https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151 IUM_02/iris.data\r\n"
     ]
    }
   ],
   "source": [
    "#Sprawdźmy wielkość zbioru\n",
    "!wc -l IUM_02/iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.1,3.5,1.4,0.2,Iris-setosa\r\n",
      "4.9,3.0,1.4,0.2,Iris-setosa\r\n",
      "4.7,3.2,1.3,0.2,Iris-setosa\r\n",
      "4.6,3.1,1.5,0.2,Iris-setosa\r\n",
      "5.0,3.6,1.4,0.2,Iris-setosa\r\n"
     ]
    }
   ],
   "source": [
    "#Sprawdźmy strukturę\n",
    "!head -n 5 IUM_02/iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      1 \r\n",
      "     50 Iris-setosa\r\n",
      "     50 Iris-versicolor\r\n",
      "     50 Iris-virginica\r\n"
     ]
    }
   ],
   "source": [
    "#Sprawdźmy jakie są klasy i ile każda ma przykładów:\n",
    "!cut -f 5 -d \",\" IUM_02/iris.data | sort | uniq -c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151:\r\n"
     ]
    }
   ],
   "source": [
    "# Znajdźmy pustą linijkę:\n",
    "! grep -P \"^$\" -n IUM_02/iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 151 IUM_02/iris.data\r\n",
      "  25 IUM_02/iris.data.dev\r\n",
      "  25 IUM_02/iris.data.test\r\n",
      " 100 IUM_02/iris.data.train\r\n",
      " 301 total\r\n"
     ]
    }
   ],
   "source": [
    "#Usuwamy pustą linijkę i tasujemy plik:\n",
    "! head -n -1 IUM_02/iris.data | shuf > IUM_02/iris.data.shuf\n",
    "# Dzielimy zbiór w proporcji 4:1:1\n",
    "!head -n 25 IUM_02/iris.data.shuf > IUM_02/iris.data.test\n",
    "!head -n 50 IUM_02/iris.data.shuf | tail -n 25 > IUM_02/iris.data.dev\n",
    "!tail -n +51 IUM_02/iris.data.shuf > IUM_02/iris.data.train\n",
    "!rm IUM_02/iris.data.shuf\n",
    "#Sprawdźmy, czy wielkości się zgadzają:\n",
    "!wc -l IUM_02/iris.data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     33 Iris-setosa\r\n",
      "     36 Iris-versicolor\r\n",
      "     31 Iris-virginica\r\n"
     ]
    }
   ],
   "source": [
    "!cut -f 5 -d \",\" IUM_02/iris.data.train | sort | uniq -c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      7 Iris-setosa\r\n",
      "      9 Iris-versicolor\r\n",
      "      9 Iris-virginica\r\n"
     ]
    }
   ],
   "source": [
    "!cut -f 5 -d \",\" IUM_02/iris.data.dev | sort | uniq -c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     10 Iris-setosa\r\n",
      "      5 Iris-versicolor\r\n",
      "     10 Iris-virginica\r\n"
     ]
    }
   ],
   "source": [
    "!cut -f 5 -d \",\" IUM_02/iris.data.test | sort | uniq -c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Podział z pomocą sckikit learn\n",
    "- Do podziału możemy też użyć biblioteki https://scikit-learn.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-virginica     36\n",
       "Iris-setosa        33\n",
       "Iris-versicolor    31\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "iris_train, iris_test = sklearn.model_selection.train_test_split(iris, test_size=50, random_state=1)\n",
    "iris_train[\"Species\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-versicolor    19\n",
       "Iris-setosa        17\n",
       "Iris-virginica     14\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_test[\"Species\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        34\n",
       "Iris-virginica     33\n",
       "Iris-versicolor    33\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "iris_train, iris_test = sklearn.model_selection.train_test_split(iris, test_size=50, random_state=1, stratify=iris[\"Species\"])\n",
    "iris_train[\"Species\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-virginica     17\n",
       "Iris-versicolor    17\n",
       "Iris-setosa        16\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_test[\"Species\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Preprocessing danych\n",
    "- Czyszczenie\n",
    "  - usuwanie ze zbioru przykładów nieprawidłowych\n",
    "  - korekta nieprawidłowych wartości\n",
    "- Normalizacja\n",
    "  - Dane numeryczne: skalowanie do zakresu, np. \\[0.0, 1.0\\] (https://scikit-learn.org/stable/modules/preprocessing.html)\n",
    "  - Dane tekstowe: lowercase, ujednolicenie wariantów pisowni, normalizacja wyrażeń numerycznych\n",
    "  - Dane obrazowe: normalizacja rozdzielczości, palety kolorów\n",
    "  - Dane dźwiękowe: normalizacja natężenia, rozdzielczości, częstotliwości próbkowania, ilości kanałów\n",
    "- Poszerzanie (augumentacja) danych\n",
    "  - Generowanie nowych przykładów przez wprowadzanie szumu/przekształceń na originalnych danych\n",
    "  - np. dodanie echa do nagrania dźwiękowego, dodanie szumów do obrazka\n",
    "  - zmiana wartości cech o względnie małe, losowe wartości \n",
    "- Over/under-sampling\n",
    "  - Algorymty uczące i metryki mogą być wrażliwe na niezbalansowane klasy w zbiorze\n",
    "  - Np. jeśli w zbiorze są 2 klasy w propocji 9:1, to najprostszy \"klasyfikator\" bez problemy osiągnie accuracy 90%\n",
    "  - Najprostszy sposób: skopiujmy (albo usuńmy) część przykładów zwiększając (lub zmniejszając) dany zbiór"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Zadanie [5pkt]\n",
    "- Wybierz jeden z ogólnodostępnych zbiorów danych. Będziesz na nim pracował do końca roku (oczywiście, zbiór można zmienić w trakcie, ale będzie się to wiązało z powtarzeniem pewnych działań, co prawdwa niezbyt kosztownych, ale jednak).\n",
    "- Zbiór powinien być:\n",
    " - nie za duży (max ~200 MB)\n",
    " - nie za mały (np. IRIS jest za mały ;))\n",
    " - unikalny (każda osoba w grupie pracuje na innym zbiorze). W celu synchronizacji, wybrany przez siebie zbiór proszę zapisać tutaj: https://uam.sharepoint.com/:x:/r/sites/2024SL06-S4IN01-F01005LABInynieriauczeniamaszynowego-Grupa11/Materiay%20z%20zaj/IUM-2024.xlsx?d=w23a1cad8c73a4fe183404d1b0671af36&csf=1&web=1&e=zUvrxN\n",
    " - najlepiej, żeby był to zbiór zawierający dane w formie tekstowej, mogący posłużyć do zadania klasyfikacji lub rergesji - na takim zbiorze będzie łatwiej pracować niż np. na zbiorze obrazów albo dźwięków. Dzięki temu będziesz się mogła/mógł skupić na istocie tych zajęć."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Napisz skrypt, który:\n",
    "1. Pobierze wybrany przez Ciebie zbiór\n",
    "2. Jeśli brak w zbiorze gotowego podziału na podzbiory train/dev/test, to dokona takiego podziału\n",
    "2. Zbierze i wydrukuje statystyki dla tego zbioru i jego podzbiorów, takie jak np.:\n",
    " - wielkość zbioru i podzbiorów\n",
    " - średnią, minimum, maksimum, odchylenia standardowe, medianę wartości poszczególnych parametrów)\n",
    " - rozkład częstości przykładów dla poszczególnych klas\n",
    "4. Dokona normalizacji danych w zbiorze (np. normalizacja wartości float do zakresu 0.0 - 1.0)\n",
    "5. Wyczyści zbiór z artefaktów (np. puste linie, przykłady z niepoprawnymi wartościami)\n",
    "\n",
    "- Skrypt możesz napisać w swoim ulubionym języku. Jedyne ograniczenie: ma działać pod Linuxem\n",
    "- Wygodnie będzie stworzyć zeszyt Jupyter w tym celu (choć nie jest to konieczne)\n",
    "- Stwórz na wydziałowym serwerze git (http://git.wmi.amu.edu.pl/) repozytorium \"ium_nrindeksu\" i umieść w nim stworzony skrypt\n",
    "- Link do repozytorium wklej do arkusza ze zbiorami: https://uam.sharepoint.com/:x:/r/sites/2024SL06-S4IN01-F01005LABInynieriauczeniamaszynowego-Grupa11/Materiay%20z%20zaj/IUM-2024.xlsx?d=w23a1cad8c73a4fe183404d1b0671af36&csf=1&web=1&e=zUvrxN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Bibliografia\n",
    " - https://www.coursera.org/learn/machine-learning-projects \n",
    " - https://see.stanford.edu/materials/aimlcs229/ML-advice.pdf\n"
   ]
  }
 ],
 "metadata": {
  "author": "Tomasz Ziętkiewicz",
  "celltoolbar": "Slideshow",
  "email": "tomasz.zietkiewicz@amu.edu.pl",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "lang": "pl",
  "language_info": {
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   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
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  "slideshow": {
   "slide_type": "slide"
  },
  "subtitle": "2.Dane[laboratoria]",
  "title": "Inżynieria uczenia maszynowego",
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
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  },
  "year": "2021"
 },
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