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+{
+ "cells": [
+ {
+ "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",
+ " - Crowdsourcing\n",
+ " - Data scrapping\n",
+ " - Ekstrakcja\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Otwarte wyzwania (shared task / challenge)\n",
+ "- Kaggle: https://www.kaggle.com/datasets\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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Repozytoria/wyszukiwarki otwartych zbiorów danych\n",
+ "- Papers with code: https://paperswithcode.com/datasets\n",
+ "- UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/\n",
+ "- Google dataset search: https://datasetsearch.research.google.com/\n",
+ "- Zbiory google:https://research.google/tools/datasets/\n",
+ "- 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 PL: https://lindat.cz/repository/xmlui/\n",
+ " - Clarin: https://clarin-pl.eu/index.php/zasoby/\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Crowdsourcing\n",
+ "- Amazon Mechanical Turk: https://www.mturk.com/\n",
+ "- Yandex Toloka\n",
+ "- reCAPTCHA\n",
+ "\n"
+ ]
+ },
+ {
+ "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",
+ "\n",
+ "https://www.kaggle.com/vinayshaw/iris-species-100-accuracy-using-naive-bayes"
+ ]
+ },
+ {
+ "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": {
+ "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": 12,
+ "metadata": {
+ "scrolled": true,
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: kaggle in /home/tomek/.local/lib/python3.8/site-packages (1.5.12)\n",
+ "Requirement already satisfied: python-dateutil in /home/tomek/anaconda3/lib/python3.8/site-packages (from kaggle) (2.8.1)\n",
+ "Requirement already satisfied: six>=1.10 in /home/tomek/anaconda3/lib/python3.8/site-packages (from kaggle) (1.15.0)\n",
+ "Requirement already satisfied: urllib3 in /home/tomek/anaconda3/lib/python3.8/site-packages (from kaggle) (1.25.11)\n",
+ "Requirement already satisfied: python-slugify in /home/tomek/.local/lib/python3.8/site-packages (from kaggle) (4.0.1)\n",
+ "Requirement already satisfied: certifi in /home/tomek/anaconda3/lib/python3.8/site-packages (from kaggle) (2020.6.20)\n",
+ "Requirement already satisfied: tqdm in /home/tomek/anaconda3/lib/python3.8/site-packages (from kaggle) (4.50.2)\n",
+ "Requirement already satisfied: requests in /home/tomek/anaconda3/lib/python3.8/site-packages (from kaggle) (2.24.0)\n",
+ "Requirement already satisfied: text-unidecode>=1.3 in /home/tomek/.local/lib/python3.8/site-packages (from python-slugify->kaggle) (1.3)\n",
+ "Requirement already satisfied: chardet<4,>=3.0.2 in /home/tomek/anaconda3/lib/python3.8/site-packages (from requests->kaggle) (3.0.4)\n",
+ "Requirement already satisfied: idna<3,>=2.5 in /home/tomek/anaconda3/lib/python3.8/site-packages (from requests->kaggle) (2.10)\n",
+ "Requirement already satisfied: pandas in /home/tomek/anaconda3/lib/python3.8/site-packages (1.1.3)\n",
+ "Requirement already satisfied: python-dateutil>=2.7.3 in /home/tomek/anaconda3/lib/python3.8/site-packages (from pandas) (2.8.1)\n",
+ "Requirement already satisfied: numpy>=1.15.4 in /home/tomek/anaconda3/lib/python3.8/site-packages (from pandas) (1.19.2)\n",
+ "Requirement already satisfied: pytz>=2017.2 in /home/tomek/anaconda3/lib/python3.8/site-packages (from pandas) (2020.1)\n",
+ "Requirement already satisfied: six>=1.5 in /home/tomek/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Zainstalujmy potrzebne biblioteki \n",
+ "!pip install --user kaggle #API Kaggle, do pobrania zbioru\n",
+ "!pip install --user pandas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /home/tomek/.kaggle/kaggle.json'\n",
+ "iris.zip: Skipping, found more recently modified local copy (use --force to force download)\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": 14,
+ "metadata": {
+ "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": "code",
+ "execution_count": 15,
+ "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": "code",
+ "execution_count": 18,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
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+ ]
+ },
+ "execution_count": 18,
+ "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": {
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+ },
+ "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": [
+ ""
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " PetalLengthCm | \n",
+ "
\n",
+ " \n",
+ " Species | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Iris-setosa | \n",
+ " 1.464 | \n",
+ "
\n",
+ " \n",
+ " Iris-versicolor | \n",
+ " 4.260 | \n",
+ "
\n",
+ " \n",
+ " Iris-virginica | \n",
+ " 5.552 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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": [
+ ""
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
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