diff --git a/.ipynb_checkpoints/aceleradev_semana2-checkpoint.ipynb b/.ipynb_checkpoints/aceleradev_semana2-checkpoint.ipynb new file mode 100644 index 0000000..4ae22e7 --- /dev/null +++ b/.ipynb_checkpoints/aceleradev_semana2-checkpoint.ipynb @@ -0,0 +1,4724 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AceleraDev Codenation - Semana 2\n", + "\n", + "### Túlio Vieira de Souza | Data Scientist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Manipulando dados" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#Importando os pacotes\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#acessando o help dos pacotes\n", + "pd?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dicionarios" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#Criando um dicionário com os dados\n", + "dados = {'canal_venda' : ['facebook', 'twitter', 'instagram', 'linkedin', 'facebook'],\n", + " 'acessos': [100, 200, 300 ,400, 500],\n", + " 'site': ['site1', 'site1', 'site2', 'site2', 'site3'],\n", + " 'vendas': [1000.52, 1052.34, 2002, 5000, 300 ]}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'canal_venda': ['facebook', 'twitter', 'instagram', 'linkedin', 'facebook'],\n", + " 'acessos': [100, 200, 300, 400, 500],\n", + " 'site': ['site1', 'site1', 'site2', 'site2', 'site3'],\n", + " 'vendas': [1000.52, 1052.34, 2002, 5000, 300]}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#para printar o dicionáario\n", + "dados" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#verificando o tipo do dicionario\n", + "type(dados)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['canal_venda', 'acessos', 'site', 'vendas'])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Acessando as chaves do meu dicionario\n", + "dados.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['site1', 'site1', 'site2', 'site2', 'site3']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Acessando uma chave especifica\n", + "dados['site']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "300" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Acessando uma posicao especifica de um dicionario\n", + "dados['acessos'][2]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'instagram'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Acessando uma posicao especifica de um dicionario\n", + "dados['canal_venda'][2]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['facebook', 'twitter', 'instagram']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Acessando uma posicao especifica de um dicionario\n", + "dados['canal_venda'][:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Listas" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "list" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type([1,2,3])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "#Criando uma lista\n", + "lista = [200, 200 , 300 ,800, 200]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[200, 200, 300, 800, 200]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#printando a lista\n", + "lista" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "200" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Vendo valores especificos\n", + "lista[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[200, 200, 300]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#fatia da lista\n", + "lista[:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Adicionando a lista ao dicionario \n", + "dados['lista'] = lista" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'canal_venda': ['facebook', 'twitter', 'instagram', 'linkedin', 'facebook'],\n", + " 'acessos': [100, 200, 300, 400, 500],\n", + " 'site': ['site1', 'site1', 'site2', 'site2', 'site3'],\n", + " 'vendas': [1000.52, 1052.34, 2002, 5000, 300],\n", + " 'lista': [200, 200, 300, 800, 200]}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DataFrames" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'canal_venda': ['facebook', 'twitter', 'instagram', 'linkedin', 'facebook'],\n", + " 'acessos': [100, 200, 300, 400, 500],\n", + " 'site': ['site1', 'site1', 'site2', 'site2', 'site3'],\n", + " 'vendas': [1000.52, 1052.34, 2002, 5000, 300],\n", + " 'lista': [200, 200, 300, 800, 200]}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dados" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "#Criar um data frame a partir de um dict\n", + "dataframe = pd.DataFrame(dados)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " canal_venda acessos site vendas lista\n", + "0 facebook 100 site1 1000.52 200\n", + "1 twitter 200 site1 1052.34 200\n", + "2 instagram 300 site2 2002.00 300\n", + "3 linkedin 400 site2 5000.00 800\n", + "4 facebook 500 site3 300.00 200" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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lista
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" + ], + "text/plain": [ + " lista\n", + "3 800\n", + "4 200" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### Fatiando os dados usando o iloc (linhas | colunas)\n", + "dataframe.iloc[3:,4:]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendaacessossitevendaslista
0facebook100site11000.52200
1twitter200site11052.34200
2instagram300site22002.00300
3linkedin400site25000.00800
4facebook500site3300.00200
\n", + "
" + ], + "text/plain": [ + " canal_venda acessos site vendas lista\n", + "0 facebook 100 site1 1000.52 200\n", + "1 twitter 200 site1 1052.34 200\n", + "2 instagram 300 site2 2002.00 300\n", + "3 linkedin 400 site2 5000.00 800\n", + "4 facebook 500 site3 300.00 200" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendaacessossitevendaslista
0facebook100site11000.52200
1twitter200site11052.34200
2instagram300site22002.00300
3linkedin400site25000.00800
\n", + "
" + ], + "text/plain": [ + " canal_venda acessos site vendas lista\n", + "0 facebook 100 site1 1000.52 200\n", + "1 twitter 200 site1 1052.34 200\n", + "2 instagram 300 site2 2002.00 300\n", + "3 linkedin 400 site2 5000.00 800" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Fatiar os dados usando o loc (indice)\n", + "dataframe.loc[:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
canal_vendavendas
0facebook1000.52
1twitter1052.34
2instagram2002.00
3linkedin5000.00
4facebook300.00
\n", + "
" + ], + "text/plain": [ + " canal_venda vendas\n", + "0 facebook 1000.52\n", + "1 twitter 1052.34\n", + "2 instagram 2002.00\n", + "3 linkedin 5000.00\n", + "4 facebook 300.00" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Selecionando colunas especificas\n", + "dataframe[['canal_venda', 'vendas']]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "#Passando os valores atraves de lista\n", + "filtro = ['canal_venda', 'acessos']" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendaacessos
0facebook100
1twitter200
2instagram300
3linkedin400
4facebook500
\n", + "
" + ], + "text/plain": [ + " canal_venda acessos\n", + "0 facebook 100\n", + "1 twitter 200\n", + "2 instagram 300\n", + "3 linkedin 400\n", + "4 facebook 500" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe[filtro]" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 5 entries, 0 to 4\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 canal_venda 5 non-null object \n", + " 1 acessos 5 non-null int64 \n", + " 2 site 5 non-null object \n", + " 3 vendas 5 non-null float64\n", + " 4 lista 5 non-null int64 \n", + "dtypes: float64(1), int64(2), object(2)\n", + "memory usage: 328.0+ bytes\n" + ] + } + ], + "source": [ + "# Usando o metodo info()\n", + "dataframe.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendaacessossitevendaslista
0facebook100site11000.52200
1twitter200site11052.34200
2instagram300site22002.00300
3linkedin400site25000.00800
4facebook500site3300.00200
\n", + "
" + ], + "text/plain": [ + " canal_venda acessos site vendas lista\n", + "0 facebook 100 site1 1000.52 200\n", + "1 twitter 200 site1 1052.34 200\n", + "2 instagram 300 site2 2002.00 300\n", + "3 linkedin 400 site2 5000.00 800\n", + "4 facebook 500 site3 300.00 200" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "#Pivotando os dados (coluna)\n", + "aux = dataframe.pivot(index = 'canal_venda', columns='site', values='acessos')" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sitesite1site2site3
canal_venda
facebook100.0NaN500.0
instagramNaN300.0NaN
linkedinNaN400.0NaN
twitter200.0NaNNaN
\n", + "
" + ], + "text/plain": [ + "site site1 site2 site3\n", + "canal_venda \n", + "facebook 100.0 NaN 500.0\n", + "instagram NaN 300.0 NaN\n", + "linkedin NaN 400.0 NaN\n", + "twitter 200.0 NaN NaN" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aux" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 4 entries, facebook to twitter\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 site1 2 non-null float64\n", + " 1 site2 2 non-null float64\n", + " 2 site3 1 non-null float64\n", + "dtypes: float64(3)\n", + "memory usage: 128.0+ bytes\n" + ] + } + ], + "source": [ + "aux.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sitesite1site2site3
canal_venda
facebook100.00.0500.0
instagram0.0300.00.0
linkedin0.0400.00.0
twitter200.00.00.0
\n", + "
" + ], + "text/plain": [ + "site site1 site2 site3\n", + "canal_venda \n", + "facebook 100.0 0.0 500.0\n", + "instagram 0.0 300.0 0.0\n", + "linkedin 0.0 400.0 0.0\n", + "twitter 200.0 0.0 0.0" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Completando os valores faltantes usando fillna\n", + "#Pivotando os dados (coluna)\n", + "aux= dataframe.pivot(index = 'canal_venda', columns='site', values='acessos').fillna(0)\n", + "dataframe.pivot(index = 'canal_venda', columns='site', values='acessos').fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendaacessossitevendaslista
0facebook100site11000.52200
1twitter200site11052.34200
2instagram300site22002.00300
3linkedin400site25000.00800
4facebook500site3300.00200
\n", + "
" + ], + "text/plain": [ + " canal_venda acessos site vendas lista\n", + "0 facebook 100 site1 1000.52 200\n", + "1 twitter 200 site1 1052.34 200\n", + "2 instagram 300 site2 2002.00 300\n", + "3 linkedin 400 site2 5000.00 800\n", + "4 facebook 500 site3 300.00 200" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
sitevariablevalue
0site1canal_vendafacebook
1site1canal_vendatwitter
2site2canal_vendainstagram
3site2canal_vendalinkedin
4site3canal_vendafacebook
\n", + "
" + ], + "text/plain": [ + " site variable value\n", + "0 site1 canal_venda facebook\n", + "1 site1 canal_venda twitter\n", + "2 site2 canal_venda instagram\n", + "3 site2 canal_venda linkedin\n", + "4 site3 canal_venda facebook" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Mudando as colunas usando o comando melt\n", + "dataframe.melt(id_vars='site', value_vars=['canal_venda'])" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['site1', 'site2', 'site3'], dtype='object', name='site')\n", + "Index(['index', 'canal_venda', 'acessos', 'site', 'vendas', 'lista'], dtype='object')\n" + ] + } + ], + "source": [ + "#Resetando o indice do dataframe\n", + "print(aux.columns)\n", + "aux = dataframe.reset_index()\n", + "print(aux.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexcanal_vendaacessossitevendaslista
00facebook100site11000.52200
11twitter200site11052.34200
22instagram300site22002.00300
33linkedin400site25000.00800
44facebook500site3300.00200
\n", + "
" + ], + "text/plain": [ + " index canal_venda acessos site vendas lista\n", + "0 0 facebook 100 site1 1000.52 200\n", + "1 1 twitter 200 site1 1052.34 200\n", + "2 2 instagram 300 site2 2002.00 300\n", + "3 3 linkedin 400 site2 5000.00 800\n", + "4 4 facebook 500 site3 300.00 200" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aux" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendaacessossitevendaslista
0facebook100site11000.52200
1twitter200site11052.34200
2instagram300site22002.00300
3linkedin400site25000.00800
4facebook500site3300.00200
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" + ], + "text/plain": [ + " canal_venda acessos site vendas lista\n", + "0 facebook 100 site1 1000.52 200\n", + "1 twitter 200 site1 1052.34 200\n", + "2 instagram 300 site2 2002.00 300\n", + "3 linkedin 400 site2 5000.00 800\n", + "4 facebook 500 site3 300.00 200" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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level_0indexcanal_vendaacessossitevendaslista
000facebook100site11000.52200
111twitter200site11052.34200
222instagram300site22002.00300
333linkedin400site25000.00800
444facebook500site3300.00200
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" + ], + "text/plain": [ + " level_0 index canal_venda acessos site vendas lista\n", + "0 0 0 facebook 100 site1 1000.52 200\n", + "1 1 1 twitter 200 site1 1052.34 200\n", + "2 2 2 instagram 300 site2 2002.00 300\n", + "3 3 3 linkedin 400 site2 5000.00 800\n", + "4 4 4 facebook 500 site3 300.00 200" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aux.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sitesite1site2site3
canal_venda
facebook100.00.0500.0
instagram0.0300.00.0
linkedin0.0400.00.0
twitter200.00.00.0
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" + ], + "text/plain": [ + "site site1 site2 site3\n", + "canal_venda \n", + "facebook 100.0 0.0 500.0\n", + "instagram 0.0 300.0 0.0\n", + "linkedin 0.0 400.0 0.0\n", + "twitter 200.0 0.0 0.0" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aux" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendasitevalue
0facebooksite1100.0
1instagramsite10.0
2linkedinsite10.0
3twittersite1200.0
4facebooksite20.0
5instagramsite2300.0
6linkedinsite2400.0
7twittersite20.0
8facebooksite3500.0
9instagramsite30.0
10linkedinsite30.0
11twittersite30.0
\n", + "
" + ], + "text/plain": [ + " canal_venda site value\n", + "0 facebook site1 100.0\n", + "1 instagram site1 0.0\n", + "2 linkedin site1 0.0\n", + "3 twitter site1 200.0\n", + "4 facebook site2 0.0\n", + "5 instagram site2 300.0\n", + "6 linkedin site2 400.0\n", + "7 twitter site2 0.0\n", + "8 facebook site3 500.0\n", + "9 instagram site3 0.0\n", + "10 linkedin site3 0.0\n", + "11 twitter site3 0.0" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Exemplo do comando melt\n", + "aux.melt(id_vars='canal_venda', value_vars=['site1', 'site2', 'site3'])" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendaacessossitevendaslista
0facebook100site11000.52200
1twitter200site11052.34200
2instagram300site22002.00300
3linkedin400site25000.00800
4facebook500site3300.00200
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" + ], + "text/plain": [ + " canal_venda acessos site vendas lista\n", + "0 facebook 100 site1 1000.52 200\n", + "1 twitter 200 site1 1052.34 200\n", + "2 instagram 300 site2 2002.00 300\n", + "3 linkedin 400 site2 5000.00 800\n", + "4 facebook 500 site3 300.00 200" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "canal_venda facebooktwitterinstagramlinkedinfacebook\n", + "acessos 1500\n", + "site site1site1site2site2site3\n", + "vendas 9354.86\n", + "lista 1700\n", + "dtype: object" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Somando as colunas do dataframe\n", + "dataframe.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendaacessossitevendaslista
0facebook100site11000.52200
1twitter200site11052.34200
2instagram300site22002.00300
3linkedin400site25000.00800
4facebook500site3300.00200
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" + ], + "text/plain": [ + " canal_venda acessos site vendas lista\n", + "0 facebook 100 site1 1000.52 200\n", + "1 twitter 200 site1 1052.34 200\n", + "2 instagram 300 site2 2002.00 300\n", + "3 linkedin 400 site2 5000.00 800\n", + "4 facebook 500 site3 300.00 200" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1300.52\n", + "1 1452.34\n", + "2 2602.00\n", + "3 6200.00\n", + "4 1000.00\n", + "dtype: float64" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Somando as linhas do dataframe\n", + "dataframe.sum(axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Por linha: 0 200.0\n", + "1 200.0\n", + "2 300.0\n", + "3 800.0\n", + "4 300.0\n", + "dtype: float64\n", + "Por coluna: acessos 300.00\n", + "vendas 1052.34\n", + "lista 200.00\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "#Calculando a mediana das colunas numericas\n", + "print('Por linha: ',dataframe.median(axis= 1) )\n", + "print('Por coluna: ', dataframe.median())" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "acessos 300.000\n", + "vendas 1870.972\n", + "lista 340.000\n", + "dtype: float64" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Calculando a media das colunas númericas\n", + "dataframe.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "acessos 158.113883\n", + "vendas 1850.931024\n", + "lista 260.768096\n", + "dtype: float64" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Calculando o desvio padrão das colunas numericas\n", + "dataframe.std()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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acessosvendaslista
count5.0000005.0000005.000000
mean300.0000001870.972000340.000000
std158.1138831850.931024260.768096
min100.000000300.000000200.000000
25%200.0000001000.520000200.000000
50%300.0000001052.340000200.000000
75%400.0000002002.000000300.000000
max500.0000005000.000000800.000000
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" + ], + "text/plain": [ + " acessos vendas lista\n", + "count 5.000000 5.000000 5.000000\n", + "mean 300.000000 1870.972000 340.000000\n", + "std 158.113883 1850.931024 260.768096\n", + "min 100.000000 300.000000 200.000000\n", + "25% 200.000000 1000.520000 200.000000\n", + "50% 300.000000 1052.340000 200.000000\n", + "75% 400.000000 2002.000000 300.000000\n", + "max 500.000000 5000.000000 800.000000" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Usando o comando describe que calcula estatisticas descritivas para colunas numericas\n", + "dataframe.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendaacessossitevendaslista
0facebook100site1300.00200.0
1NaN200site21000.52NaN
2NaN300NaN1052.34NaN
3NaN400NaN2002.00NaN
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" + ], + "text/plain": [ + " canal_venda acessos site vendas lista\n", + "0 facebook 100 site1 300.00 200.0\n", + "1 NaN 200 site2 1000.52 NaN\n", + "2 NaN 300 NaN 1052.34 NaN\n", + "3 NaN 400 NaN 2002.00 NaN\n", + "4 NaN 500 NaN 5000.00 NaN" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Calculando a moda\n", + "dataframe.mode()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "canal_venda twitter\n", + "acessos 500\n", + "site site3\n", + "vendas 5000\n", + "lista 800\n", + "dtype: object" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Valor maximo por coluna\n", + "dataframe.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "canal_venda facebook\n", + "acessos 100\n", + "site site1\n", + "vendas 300\n", + "lista 200\n", + "dtype: object" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Valor minimo por coluna\n", + "dataframe.min()" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "canal_venda 4\n", + "acessos 5\n", + "site 3\n", + "vendas 5\n", + "lista 3\n", + "dtype: int64" + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Printando o numero de unicos\n", + "dataframe.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "facebook 2\n", + "linkedin 1\n", + "instagram 1\n", + "twitter 1\n", + "Name: canal_venda, dtype: int64" + ] + }, + "execution_count": 221, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Contando valores unicos de uma coluna\n", + "dataframe['canal_venda'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['facebook', 'twitter', 'instagram', 'linkedin'], dtype=object)" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Valores unicos de uma coluna\n", + "dataframe['canal_venda'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "site\n", + "site1 300\n", + "site2 700\n", + "site3 500\n", + "Name: acessos, dtype: int64" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Usando o groupby com valores numericos\n", + "dataframe.groupby('site')['acessos'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "canal_venda\n", + "facebook 300\n", + "instagram 300\n", + "linkedin 400\n", + "twitter 200\n", + "Name: acessos, dtype: int64" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Usando o groupby com valores numericos\n", + "dataframe.groupby('canal_venda')['acessos'].median()" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "site\n", + "site1 [facebook, twitter]\n", + "site2 [instagram, linkedin]\n", + "site3 [facebook]\n", + "Name: canal_venda, dtype: object" + ] + }, + "execution_count": 229, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Usando o groupby com categoricos\n", + "dataframe.groupby('site')['canal_venda'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "site\n", + "site1 facebook\n", + "site2 instagram\n", + "site3 facebook\n", + "Name: canal_venda, dtype: object" + ] + }, + "execution_count": 230, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Usando o groupby com categoricos\n", + "dataframe.groupby('site')['canal_venda'].first()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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acessosvendaslista
acessos1.000000.0000000.335410
vendas0.000001.0000000.894427
lista0.335410.8944271.000000
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canal_vendaacessossitevendaslista
0facebook100site11000.52200
1twitter200site11052.34200
2instagram300site22002.00300
3linkedin400site25000.00800
4facebook500site3300.00200
\n", + "
" + ], + "text/plain": [ + " canal_venda acessos site vendas lista\n", + "0 facebook 100 site1 1000.52 200\n", + "1 twitter 200 site1 1052.34 200\n", + "2 instagram 300 site2 2002.00 300\n", + "3 linkedin 400 site2 5000.00 800\n", + "4 facebook 500 site3 300.00 200" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "#Criando variaveis categoricas por fatia de variavel numerica\n", + "dataframe['categoria_vendas'] = pd.cut(dataframe['vendas'],\n", + " bins= (0, 1500, 2000, 8000), \n", + " labels = ('0 a 1500', '1500 a 2000', '2000 a 8000'))" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendaacessossitevendaslistacategoria_vendas
0facebook100site11000.522000 a 1500
1twitter200site11052.342000 a 1500
2instagram300site22002.003002000 a 8000
3linkedin400site25000.008002000 a 8000
4facebook500site3300.002000 a 1500
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" + ], + "text/plain": [ + " canal_venda acessos site vendas lista categoria_vendas\n", + "0 facebook 100 site1 1000.52 200 0 a 1500\n", + "1 twitter 200 site1 1052.34 200 0 a 1500\n", + "2 instagram 300 site2 2002.00 300 2000 a 8000\n", + "3 linkedin 400 site2 5000.00 800 2000 a 8000\n", + "4 facebook 500 site3 300.00 200 0 a 1500" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [], + "source": [ + "#Criando variavel categorica usando compressao de lista\n", + "dataframe['categoria_acessos'] = ['maior_que_300' if x > 300 else 'menor_que_300' for x in dataframe['acessos']]" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendaacessossitevendaslistacategoria_vendascategoria_acessos
0facebook100site11000.522000 a 1500menor_que_300
1twitter200site11052.342000 a 1500menor_que_300
2instagram300site22002.003002000 a 8000menor_que_300
3linkedin400site25000.008002000 a 8000maior_que_300
4facebook500site3300.002000 a 1500maior_que_300
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" + ], + "text/plain": [ + " canal_venda acessos site vendas lista categoria_vendas \\\n", + "0 facebook 100 site1 1000.52 200 0 a 1500 \n", + "1 twitter 200 site1 1052.34 200 0 a 1500 \n", + "2 instagram 300 site2 2002.00 300 2000 a 8000 \n", + "3 linkedin 400 site2 5000.00 800 2000 a 8000 \n", + "4 facebook 500 site3 300.00 200 0 a 1500 \n", + "\n", + " categoria_acessos \n", + "0 menor_que_300 \n", + "1 menor_que_300 \n", + "2 menor_que_300 \n", + "3 maior_que_300 \n", + "4 maior_que_300 " + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "#Juntando dois dataframes | Criando o dataframe_2\n", + "dataframe_2 = pd.DataFrame({'site': ['site1', 'site1', 'site2', 'site2', 'site3'],\n", + " 'suporte': ['Carlos', 'Carlos', 'Maria', 'Maria', 'Ezequiel']})\n" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendaacessossitevendaslistacategoria_vendascategoria_acessossuporte
0facebook100site11000.522000 a 1500menor_que_300Carlos
1facebook100site11000.522000 a 1500menor_que_300Carlos
2twitter200site11052.342000 a 1500menor_que_300Carlos
3twitter200site11052.342000 a 1500menor_que_300Carlos
4instagram300site22002.003002000 a 8000menor_que_300Maria
5instagram300site22002.003002000 a 8000menor_que_300Maria
6linkedin400site25000.008002000 a 8000maior_que_300Maria
7linkedin400site25000.008002000 a 8000maior_que_300Maria
8facebook500site3300.002000 a 1500maior_que_300Ezequiel
\n", + "
" + ], + "text/plain": [ + " canal_venda acessos site vendas lista categoria_vendas \\\n", + "0 facebook 100 site1 1000.52 200 0 a 1500 \n", + "1 facebook 100 site1 1000.52 200 0 a 1500 \n", + "2 twitter 200 site1 1052.34 200 0 a 1500 \n", + "3 twitter 200 site1 1052.34 200 0 a 1500 \n", + "4 instagram 300 site2 2002.00 300 2000 a 8000 \n", + "5 instagram 300 site2 2002.00 300 2000 a 8000 \n", + "6 linkedin 400 site2 5000.00 800 2000 a 8000 \n", + "7 linkedin 400 site2 5000.00 800 2000 a 8000 \n", + "8 facebook 500 site3 300.00 200 0 a 1500 \n", + "\n", + " categoria_acessos suporte \n", + "0 menor_que_300 Carlos \n", + "1 menor_que_300 Carlos \n", + "2 menor_que_300 Carlos \n", + "3 menor_que_300 Carlos \n", + "4 menor_que_300 Maria \n", + "5 menor_que_300 Maria \n", + "6 maior_que_300 Maria \n", + "7 maior_que_300 Maria \n", + "8 maior_que_300 Ezequiel " + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Realizando o merge\n", + "dataframe.merge(dataframe_2, on = 'site', how = 'left')" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [], + "source": [ + "#Salvando o dataframe como csv\n", + "dataframe.to_csv('dataframe.csv', sep = ';', decimal = ',', index = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "#Lendo dados no formato csv\n", + "dataframe_lido = pd.read_csv('dataframe.csv', sep = ';', decimal = ',')" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_vendaacessossitevendaslistacategoria_vendascategoria_acessos
0facebook100site11000.522000 a 1500menor_que_300
1twitter200site11052.342000 a 1500menor_que_300
2instagram300site22002.003002000 a 8000menor_que_300
3linkedin400site25000.008002000 a 8000maior_que_300
4facebook500site3300.002000 a 1500maior_que_300
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" + ], + "text/plain": [ + " canal_venda acessos site vendas lista categoria_vendas \\\n", + "0 facebook 100 site1 1000.52 200 0 a 1500 \n", + "1 twitter 200 site1 1052.34 200 0 a 1500 \n", + "2 instagram 300 site2 2002.00 300 2000 a 8000 \n", + "3 linkedin 400 site2 5000.00 800 2000 a 8000 \n", + "4 facebook 500 site3 300.00 200 0 a 1500 \n", + "\n", + " categoria_acessos \n", + "0 menor_que_300 \n", + "1 menor_que_300 \n", + "2 menor_que_300 \n", + "3 maior_que_300 \n", + "4 maior_que_300 " + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe_lido.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "json = pd.read_json('https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/index.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'offerCode': 'AmazonMQ',\n", + " 'versionIndexUrl': '/offers/v1.0/aws/AmazonMQ/index.json',\n", + " 'currentVersionUrl': '/offers/v1.0/aws/AmazonMQ/current/index.json',\n", + " 'currentRegionIndexUrl': '/offers/v1.0/aws/AmazonMQ/current/region_index.json'}" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json['offers'][99]" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "json.reset_index(inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 A4B\n", + "1 AWSAmplify\n", + "2 AWSAppSync\n", + "3 AWSBackup\n", + "4 AWSBudgets\n", + " ... \n", + "143 comprehendmedical\n", + "144 datapipeline\n", + "145 mobileanalytics\n", + "146 transcribe\n", + "147 translate\n", + "Name: index, Length: 148, dtype: object" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json['index']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imputação de dados" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('train.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "aux = pd.DataFrame({'colunas': df.columns,\n", + " 'tipos': df.dtypes,\n", + " 'percentual_faltante': df.isna().sum() / df.shape[0]})" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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colunastipospercentual_faltante
PassengerIdPassengerIdint640.000000
SurvivedSurvivedint640.000000
PclassPclassint640.000000
NameNameobject0.000000
SexSexobject0.000000
AgeAgefloat640.000000
SibSpSibSpint640.000000
ParchParchint640.000000
TicketTicketobject0.000000
FareFarefloat640.000000
CabinCabinobject0.771044
EmbarkedEmbarkedobject0.002245
\n", + "
" + ], + "text/plain": [ + " colunas tipos percentual_faltante\n", + "PassengerId PassengerId int64 0.000000\n", + "Survived Survived int64 0.000000\n", + "Pclass Pclass int64 0.000000\n", + "Name Name object 0.000000\n", + "Sex Sex object 0.000000\n", + "Age Age float64 0.000000\n", + "SibSp SibSp int64 0.000000\n", + "Parch Parch int64 0.000000\n", + "Ticket Ticket object 0.000000\n", + "Fare Fare float64 0.000000\n", + "Cabin Cabin object 0.771044\n", + "Embarked Embarked object 0.002245" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aux" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "#Dados númericos : média ou mediana\n", + "df['Age'] = df['Age'].fillna(df['Age'].mode())" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "#Dados categoricos: Unknown ou moda\n", + "df['Cabin'] = df['Cabin'].fillna('Unknown')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Unknown 687\n", + "B96 B98 4\n", + "C23 C25 C27 4\n", + "G6 4\n", + "E101 3\n", + " ... \n", + "C85 1\n", + "C148 1\n", + "A36 1\n", + "B3 1\n", + "D28 1\n", + "Name: Cabin, Length: 148, dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Cabin'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "891" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.ipynb_checkpoints/semana_3-checkpoint.ipynb b/.ipynb_checkpoints/semana_3-checkpoint.ipynb new file mode 100644 index 0000000..4207360 --- /dev/null +++ b/.ipynb_checkpoints/semana_3-checkpoint.ipynb @@ -0,0 +1,1179 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Aceleradev Codenation - Semana 3\n", + "## Túlio Vieira de Souza | Data Scientist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "dados : https://www.kaggle.com/rubenssjr/brasilian-houses-to-rent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Colunas \n", + "\n", + "- city : Cidade onde o imóvel está localizada / City where the property is located\n", + "- area : Area do imovel / Property area\n", + "- rooms: Numero de quartos/ Quantity of rooms\n", + "- bathroom: Numero de banheiros / Quantity of bathroom\n", + "- parking spaces : Numero de vagas / Quantity of parking spaces\n", + "- floor : Andar / Floor\n", + "- animal : Aceita animais? / Acept animals?\n", + "- furniture : Mobilhada? / Furniture?\n", + "- hoa (RS): Valor do condomínio / Homeowners association tax \n", + "- rent amount (RS) : Valor do Aluguel (/) Rent amount \n", + "- property tax (RS) : IPTU (/) Property tax\n", + "- fire insurance (RS) : Seguro Incendio / Fire Insurance\n", + "- total (RS) : Valor total / Total" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importando os pacotes" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('houses_to_rent_v2.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cityarearoomsbathroomparking spacesflooranimalfurniturehoa (R$)rent amount (R$)property tax (R$)fire insurance (R$)total (R$)
0São Paulo702117aceptfurnished20653300211425618
1São Paulo32044020aceptnot furnished120049601750637973
2Porto Alegre801116aceptnot furnished100028000413841
3Porto Alegre512102aceptnot furnished270111222171421
4São Paulo251101not aceptnot furnished08002511836
\n", + "
" + ], + "text/plain": [ + " city area rooms bathroom parking spaces floor animal \\\n", + "0 São Paulo 70 2 1 1 7 acept \n", + "1 São Paulo 320 4 4 0 20 acept \n", + "2 Porto Alegre 80 1 1 1 6 acept \n", + "3 Porto Alegre 51 2 1 0 2 acept \n", + "4 São Paulo 25 1 1 0 1 not acept \n", + "\n", + " furniture hoa (R$) rent amount (R$) property tax (R$) \\\n", + "0 furnished 2065 3300 211 \n", + "1 not furnished 1200 4960 1750 \n", + "2 not furnished 1000 2800 0 \n", + "3 not furnished 270 1112 22 \n", + "4 not furnished 0 800 25 \n", + "\n", + " fire insurance (R$) total (R$) \n", + "0 42 5618 \n", + "1 63 7973 \n", + "2 41 3841 \n", + "3 17 1421 \n", + "4 11 836 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "city object\n", + "area int64\n", + "rooms int64\n", + "bathroom int64\n", + "parking spaces int64\n", + "floor object\n", + "animal object\n", + "furniture object\n", + "hoa (R$) int64\n", + "rent amount (R$) int64\n", + "property tax (R$) int64\n", + "fire insurance (R$) int64\n", + "total (R$) int64\n", + "dtype: object" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 10692 entries, 0 to 10691\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 city 10692 non-null object\n", + " 1 area 10692 non-null int64 \n", + " 2 rooms 10692 non-null int64 \n", + " 3 bathroom 10692 non-null int64 \n", + " 4 parking spaces 10692 non-null int64 \n", + " 5 floor 10692 non-null object\n", + " 6 animal 10692 non-null object\n", + " 7 furniture 10692 non-null object\n", + " 8 hoa (R$) 10692 non-null int64 \n", + " 9 rent amount (R$) 10692 non-null int64 \n", + " 10 property tax (R$) 10692 non-null int64 \n", + " 11 fire insurance (R$) 10692 non-null int64 \n", + " 12 total (R$) 10692 non-null int64 \n", + "dtypes: int64(9), object(4)\n", + "memory usage: 1.1+ MB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Problema : Explorar o valor do aluguel (rent amount RS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estatística univariada" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "df.rename(columns = {'rent amount (R$)' : 'valor_aluguel'}, inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3896.247194163861" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['valor_aluguel'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2661.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['valor_aluguel'].median()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3408.5455176710816" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['valor_aluguel'].std()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 10692.000000\n", + "mean 3896.247194\n", + "std 3408.545518\n", + "min 450.000000\n", + "25% 1530.000000\n", + "50% 2661.000000\n", + "75% 5000.000000\n", + "max 45000.000000\n", + "Name: valor_aluguel, dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['valor_aluguel'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " city valor_aluguel\n", + "4 São Paulo 3400\n", + "0 Belo Horizonte 2300\n", + "3 Rio de Janeiro 2300\n", + "2 Porto Alegre 1650\n", + "1 Campinas 1500" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Qual a cidade com a média de aluguel mais alta? \n", + "\n", + "df.groupby('city')['valor_aluguel'].median().reset_index().sort_values('valor_aluguel', ascending = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#Quantos banheiros existem em média nas residencias com alugueis mais altos? \n", + "## definicao: algueis mais altos são valores acima de 5000\n", + "\n", + "df['aluguel_alto'] = ['Alto' if x > 5000 else 'Baixo' for x in df['valor_aluguel']]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "aluguel_alto\n", + "Alto 3.729027\n", + "Baixo 1.772108\n", + "Name: bathroom, dtype: float64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('aluguel_alto')['bathroom'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hipóteses\n", + "\n", + "- São Paulo é a cidade com o aluguel mais caro.\n", + "- Quanto mais banheiros em um imovel maior o valor do aluguel.\n", + "- Os imoveis com mobilia tem o aluguel mais alto. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " valor_aluguel bathroom\n", + "valor_aluguel 1.00000 0.71589\n", + "bathroom 0.71589 1.00000" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[['valor_aluguel', 'bathroom']].corr(method = 'spearman')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "aux = pd.DataFrame({'colunas' : df.columns, 'tipos' : df.dtypes})" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "lista = list(aux[aux['tipos'] == 'int64']['colunas'])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "area\n", + " valor_aluguel area\n", + "valor_aluguel 1.000000 0.728095\n", + "area 0.728095 1.000000\n", + "rooms\n", + " valor_aluguel rooms\n", + "valor_aluguel 1.000000 0.600969\n", + "rooms 0.600969 1.000000\n", + "bathroom\n", + " valor_aluguel bathroom\n", + "valor_aluguel 1.00000 0.71589\n", + "bathroom 0.71589 1.00000\n", + "parking spaces\n", + " valor_aluguel parking spaces\n", + "valor_aluguel 1.000000 0.620175\n", + "parking spaces 0.620175 1.000000\n", + "hoa (R$)\n", + " valor_aluguel hoa (R$)\n", + "valor_aluguel 1.000000 0.355785\n", + "hoa (R$) 0.355785 1.000000\n", + "valor_aluguel\n", + " valor_aluguel valor_aluguel\n", + "valor_aluguel 1.0 1.0\n", + "valor_aluguel 1.0 1.0\n", + "property tax (R$)\n", + " valor_aluguel property tax (R$)\n", + "valor_aluguel 1.00000 0.65923\n", + "property tax (R$) 0.65923 1.00000\n", + "fire insurance (R$)\n", + " valor_aluguel fire insurance (R$)\n", + "valor_aluguel 1.000000 0.988045\n", + "fire insurance (R$) 0.988045 1.000000\n", + "total (R$)\n", + " valor_aluguel total (R$)\n", + "valor_aluguel 1.000000 0.968176\n", + "total (R$) 0.968176 1.000000\n" + ] + } + ], + "source": [ + "for coluna in lista:\n", + " print(coluna)\n", + " print(df[['valor_aluguel', coluna]].corr(method = 'spearman'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualização de dados" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "São Paulo 1857\n", + "Belo Horizonte 276\n", + "Rio de Janeiro 229\n", + "Porto Alegre 89\n", + "Campinas 88\n", + "Name: city, dtype: int64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.query('aluguel_alto==\"Alto\"').city.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#resetando o index, colocou o eixo x em ordem alfabética e principalmente a média\n", + "sns.barplot(x='city', y='valor_aluguel', data=df.groupby('city')['valor_aluguel'].mean().reset_index())" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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" + ], + "text/plain": [ + " aluguel_alto valor_aluguel city\n", + "0 Alto 5010 1\n", + "1 Alto 5015 2\n", + "2 Alto 5025 1\n", + "3 Alto 5050 1\n", + "4 Alto 5058 1\n", + ".. ... ... ...\n", + "880 Baixo 4960 2\n", + "881 Baixo 4990 1\n", + "882 Baixo 4998 1\n", + "883 Baixo 4999 1\n", + "884 Baixo 5000 118\n", + "\n", + "[885 rows x 3 columns]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.query('city==\"São Paulo\"').groupby(['aluguel_alto','valor_aluguel'])['city'].count().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..67a3cc4 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "python.pythonPath": "C:\\Users\\marce\\.conda\\envs\\aceleradev_ds\\python.exe" +} \ No newline at end of file diff --git a/Semana 2/aceleradev_semana2.ipynb b/Semana 2/aceleradev_semana2.ipynb index ed58c0c..4ae22e7 100644 --- a/Semana 2/aceleradev_semana2.ipynb +++ b/Semana 2/aceleradev_semana2.ipynb @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -71,7 +71,7 @@ " 'vendas': [1000.52, 1052.34, 2002, 5000, 300]}" ] }, - "execution_count": 7, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -92,7 +92,7 @@ "dict" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -113,7 +113,7 @@ "dict_keys(['canal_venda', 'acessos', 'site', 'vendas'])" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -125,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -134,7 +134,7 @@ "['site1', 'site1', 'site2', 'site2', 'site3']" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -146,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -155,7 +155,7 @@ "300" ] }, - "execution_count": 16, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -176,7 +176,7 @@ "'instagram'" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -197,7 +197,7 @@ "['facebook', 'twitter', 'instagram']" ] }, - "execution_count": 19, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -216,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -225,7 +225,7 @@ "list" ] }, - "execution_count": 21, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -236,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -255,7 +255,7 @@ "[200, 200, 300, 800, 200]" ] }, - "execution_count": 23, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -267,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -276,7 +276,7 @@ "200" ] }, - "execution_count": 24, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -288,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -297,7 +297,7 @@ "[200, 200, 300]" ] }, - "execution_count": 25, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -309,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -319,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -332,7 +332,7 @@ " 'lista': [200, 200, 300, 800, 200]}" ] }, - "execution_count": 27, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -350,7 +350,27 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -363,7 +383,7 @@ " 'lista': [200, 200, 300, 800, 200]}" ] }, - "execution_count": 29, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -374,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -384,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -469,7 +489,7 @@ "4 facebook 500 site3 300.00 200" ] }, - "execution_count": 31, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -481,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -539,7 +559,7 @@ "1 twitter 200 site1 1052.34 200" ] }, - "execution_count": 35, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -551,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -560,7 +580,7 @@ "(5, 5)" ] }, - "execution_count": 36, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -572,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -581,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -590,7 +610,7 @@ "(5, 5)" ] }, - "execution_count": 38, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -601,7 +621,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -610,7 +630,7 @@ "pandas.core.frame.DataFrame" ] }, - "execution_count": 39, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -621,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -630,7 +650,7 @@ "RangeIndex(start=0, stop=5, step=1)" ] }, - "execution_count": 40, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -642,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -656,7 +676,7 @@ "dtype: object" ] }, - "execution_count": 43, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -668,7 +688,31 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "object 2\n", + "int64 2\n", + "float64 1\n", + "dtype: int64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#saber quantas colunas tem de cada tipo\n", + "dataframe.dtypes.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -753,7 +797,7 @@ "4 False False False False False" ] }, - "execution_count": 49, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -765,7 +809,32 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "canal_venda 0\n", + "acessos 0\n", + "site 0\n", + "vendas 0\n", + "lista 0\n", + "dtype: int64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframe.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -774,7 +843,7 @@ "Index(['canal_venda', 'acessos', 'site', 'vendas', 'lista'], dtype='object')" ] }, - "execution_count": 23, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -786,16 +855,21 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['facebook', 'twitter', 'instagram', 'linkedin', 'facebook']" + "0 facebook\n", + "1 twitter\n", + "2 instagram\n", + "3 linkedin\n", + "4 facebook\n", + "Name: canal_venda, dtype: object" ] }, - "execution_count": 52, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -807,7 +881,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -817,7 +891,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -908,7 +982,7 @@ "4 facebook 500 site3 300.00 200 5" ] }, - "execution_count": 54, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -919,7 +993,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -928,7 +1002,7 @@ "Index(['canal_venda', 'acessos', 'site', 'vendas', 'lista', 'nova_coluna'], dtype='object')" ] }, - "execution_count": 55, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -939,7 +1013,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -965,6 +1039,7 @@ " \n", " vendas\n", " lista\n", + " nova_coluna\n", " \n", " \n", " \n", @@ -972,53 +1047,58 @@ " 0\n", " 1000.52\n", " 200\n", + " 1\n", " \n", " \n", " 1\n", " 1052.34\n", " 200\n", + " 2\n", " \n", " \n", " 2\n", " 2002.00\n", " 300\n", + " 3\n", " \n", " \n", " 3\n", " 5000.00\n", " 800\n", + " 4\n", " \n", " \n", " 4\n", " 300.00\n", " 200\n", + " 5\n", " \n", " \n", "\n", "" ], "text/plain": [ - " vendas lista\n", - "0 1000.52 200\n", - "1 1052.34 200\n", - "2 2002.00 300\n", - "3 5000.00 800\n", - "4 300.00 200" + " vendas lista nova_coluna\n", + "0 1000.52 200 1\n", + "1 1052.34 200 2\n", + "2 2002.00 300 3\n", + "3 5000.00 800 4\n", + "4 300.00 200 5" ] }, - "execution_count": 65, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Removendo colunas\n", + "# Removendo colunas somente no plotter na tela\n", "dataframe.drop(columns = ['acessos','site', 'canal_venda'])" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1047,6 +1127,7 @@ " site\n", " vendas\n", " lista\n", + " nova_coluna\n", " \n", " \n", " \n", @@ -1057,6 +1138,7 @@ " site1\n", " 1000.52\n", " 200\n", + " 1\n", " \n", " \n", " 1\n", @@ -1065,6 +1147,7 @@ " site1\n", " 1052.34\n", " 200\n", + " 2\n", " \n", " \n", " 2\n", @@ -1073,6 +1156,7 @@ " site2\n", " 2002.00\n", " 300\n", + " 3\n", " \n", " \n", " 3\n", @@ -1081,6 +1165,7 @@ " site2\n", " 5000.00\n", " 800\n", + " 4\n", " \n", " \n", " 4\n", @@ -1089,21 +1174,22 @@ " site3\n", " 300.00\n", " 200\n", + " 5\n", " \n", " \n", "\n", "" ], "text/plain": [ - " canal_venda acessos site vendas lista\n", - "0 facebook 100 site1 1000.52 200\n", - "1 twitter 200 site1 1052.34 200\n", - "2 instagram 300 site2 2002.00 300\n", - "3 linkedin 400 site2 5000.00 800\n", - "4 facebook 500 site3 300.00 200" + " canal_venda acessos site vendas lista nova_coluna\n", + "0 facebook 100 site1 1000.52 200 1\n", + "1 twitter 200 site1 1052.34 200 2\n", + "2 instagram 300 site2 2002.00 300 3\n", + "3 linkedin 400 site2 5000.00 800 4\n", + "4 facebook 500 site3 300.00 200 5" ] }, - "execution_count": 66, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1114,7 +1200,16 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "dataframe.drop(columns='nova_coluna',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1123,7 +1218,7 @@ "Index(['canal_venda', 'acessos', 'site', 'vendas', 'lista'], dtype='object')" ] }, - "execution_count": 28, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1135,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1144,7 +1239,7 @@ "200" ] }, - "execution_count": 71, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1156,7 +1251,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1167,7 +1262,7 @@ "Name: canal_venda, dtype: object" ] }, - "execution_count": 30, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1179,7 +1274,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1264,7 +1359,7 @@ "4 facebook 500 site3 300.00 200" ] }, - "execution_count": 74, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1275,7 +1370,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1321,7 +1416,7 @@ "4 200" ] }, - "execution_count": 79, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1333,7 +1428,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1418,7 +1513,7 @@ "4 facebook 500 site3 300.00 200" ] }, - "execution_count": 80, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1429,7 +1524,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1505,7 +1600,7 @@ "3 linkedin 400 site2 5000.00 800" ] }, - "execution_count": 32, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1517,7 +1612,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1584,7 +1679,7 @@ "4 facebook 300.00" ] }, - "execution_count": 83, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1596,7 +1691,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1606,7 +1701,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1673,7 +1768,7 @@ "4 facebook 500" ] }, - "execution_count": 85, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1684,7 +1779,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1713,7 +1808,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -1798,7 +1893,7 @@ "4 facebook 500 site3 300.00 200" ] }, - "execution_count": 87, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -1809,7 +1904,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1819,7 +1914,91 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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canal_venda
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instagramNaN300.0NaN
linkedinNaN400.0NaN
twitter200.0NaNNaN
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indexcanal_vendaacessossitevendaslista
00facebook100site11000.52200
11twitter200site11052.34200
22instagram300site22002.00300
33linkedin400site25000.00800
44facebook500site3300.00200
\n", + "
" + ], + "text/plain": [ + " index canal_venda acessos site vendas lista\n", + "0 0 facebook 100 site1 1000.52 200\n", + "1 1 twitter 200 site1 1052.34 200\n", + "2 2 instagram 300 site2 2002.00 300\n", + "3 3 linkedin 400 site2 5000.00 800\n", + "4 4 facebook 500 site3 300.00 200" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aux" + ] + }, + { + "cell_type": "code", + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -2219,7 +2500,7 @@ "4 facebook 500 site3 300.00 200" ] }, - "execution_count": 96, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -4435,7 +4716,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.7.6" } }, "nbformat": 4, diff --git a/Semana 3/semana_3.ipynb b/Semana 3/semana_3.ipynb index d7939e4..4207360 100644 --- a/Semana 3/semana_3.ipynb +++ b/Semana 3/semana_3.ipynb @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -210,7 +210,7 @@ "4 11 836 " ] }, - "execution_count": 30, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -221,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -243,7 +243,7 @@ "dtype: object" ] }, - "execution_count": 31, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -254,10 +254,8 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -306,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -315,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -324,7 +322,7 @@ "3896.247194163861" ] }, - "execution_count": 42, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -335,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -344,7 +342,7 @@ "2661.0" ] }, - "execution_count": 43, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -355,16 +353,16 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3408.5455176710675" + "3408.5455176710816" ] }, - "execution_count": 44, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -375,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -392,7 +390,7 @@ "Name: valor_aluguel, dtype: float64" ] }, - "execution_count": 45, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -403,22 +401,22 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 51, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -435,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -444,7 +442,7 @@ "1.8388773035440982" ] }, - "execution_count": 56, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -456,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -465,7 +463,7 @@ "4.624228179818687" ] }, - "execution_count": 57, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -496,7 +494,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -563,7 +561,7 @@ "1 Campinas 1500" ] }, - "execution_count": 64, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -576,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -588,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -600,7 +598,7 @@ "Name: bathroom, dtype: float64" ] }, - "execution_count": 71, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -622,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -671,7 +669,7 @@ "bathroom 0.71589 1.00000" ] }, - "execution_count": 79, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -682,7 +680,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -691,7 +689,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -700,7 +698,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -761,45 +759,393 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "São Paulo 1857\n", + "Belo Horizonte 276\n", + "Rio de Janeiro 229\n", + "Porto Alegre 89\n", + "Campinas 88\n", + "Name: city, dtype: int64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.query('aluguel_alto==\"Alto\"').city.value_counts()" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#resetando o index, colocou o eixo x em ordem alfabética e principalmente a média\n", + "sns.barplot(x='city', y='valor_aluguel', data=df.groupby('city')['valor_aluguel'].mean().reset_index())" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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" + ], + "text/plain": [ + " aluguel_alto valor_aluguel city\n", + "0 Alto 5010 1\n", + "1 Alto 5015 2\n", + "2 Alto 5025 1\n", + "3 Alto 5050 1\n", + "4 Alto 5058 1\n", + ".. ... ... ...\n", + "880 Baixo 4960 2\n", + "881 Baixo 4990 1\n", + "882 Baixo 4998 1\n", + "883 Baixo 4999 1\n", + "884 Baixo 5000 118\n", + "\n", + "[885 rows x 3 columns]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.query('city==\"São Paulo\"').groupby(['aluguel_alto','valor_aluguel'])['city'].count().reset_index()" + ] }, { "cell_type": "code", @@ -825,7 +1171,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.7.6" } }, "nbformat": 4, diff --git a/Semana 6/.ipynb_checkpoints/Semana 6 - Aceleradev-checkpoint.ipynb b/Semana 6/.ipynb_checkpoints/Semana 6 - Aceleradev-checkpoint.ipynb new file mode 100644 index 0000000..8538c62 --- /dev/null +++ b/Semana 6/.ipynb_checkpoints/Semana 6 - Aceleradev-checkpoint.ipynb @@ -0,0 +1,1873 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AceleraDev DataScience \n", + "\n", + "## Setup\n", + "\n", + "https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "#lendo os pacotes\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('train.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Selecao por completude" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "#Criando um dataframe auxliar para analisar a consistencia das variaveis\n", + "cons = pd.DataFrame({'colunas' : df.columns,\n", + " 'tipo': df.dtypes,\n", + " 'missing' : df.isna().sum(),\n", + " 'size' : df.shape[0],\n", + " 'unicos': df.nunique()})\n", + "cons['percentual'] = round(cons['missing'] / cons['size'],2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "cons.percentual.plot.hist( bins = 5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removendo colunas com dados missing" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Contagem de colunas com ATÉ 20% de dados faltantes 76\n", + "Contagem de colunas com 0% de dados faltantes 63\n" + ] + } + ], + "source": [ + "print('Contagem de colunas com ATÉ 20% de dados faltantes', cons[cons.percentual < 0.2].shape[0])\n", + "print('Contagem de colunas com 0% de dados faltantes', cons[cons.percentual == 0].shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "int64 35\n", + "object 28\n", + "Name: tipo, dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cons[cons.percentual == 0]['tipo'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "cons['completa'] = ['completa' if x == 0 else 'faltante' for x in cons['percentual']]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "mantem = list(cons[cons['completa'] == 'completa']['colunas'])\n", + "df = df[mantem]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "colunas_numericas = list(cons[((cons['tipo'] != 'object') &\n", + " (cons['completa'] == 'completa'))]['colunas'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploração" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Id\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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ox6wBrgWeDaPaL136MZb9cgpwU5J1TELr5qr6+DQ/v7x0hSQJmP9DRpKkjgwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSp+V8waGCceUtI7wAAAABJRU5ErkJggg==\n", 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\n", 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H0I/VwB1z9u8GntptPxW4u9v+ALBxvtdNwgP4N+AV09Y/4GjgVuD5jM6EPbxr77+fjC4v/8Ju+/DudRm69v30aRWjX44vBz4DZIr6tgM4Zp+2qfpO7n3M3EiBhe8RPemeUlXf7bb/C3hKtz2x/e2mFJ4LfIUp6V83vXIbsAu4DvgW8KMa3WcEfrn+vm/d83uAJy1txQfl3cBfAT/v9p/E9PStgM8luSXJpq5tKr6T+1o292jW4qmqSjLRxxon+TXgE8BbquqBJP1zk9y/qnoYeE6SJwCfAp4+cEmLIsnvA7uq6pYkLx26ngZeXFX3J3kycF2Sb859cpK/k/uaxZHCI75H9DL130meCtD9uatrn7j+Jnk0o0D4SFV9smuemv4BVNWPgC8ymlJ5QpK9/4M2t/6+b93zK4DvL3Gp43oRcEaSHcAVjKaQ3sN09I2qur/7cxejMD+VKftO7jWLofCI7xG9TF0FvKHbfgOjufi97X/cHRHxAmDPnCHvspPRkOCDwF1V9a45T018/5Ks7EYIJDmK0VrJXYzC4azuZfv2bW+fzwK+UN0k9XJTVRdU1aqqWs3ov6kvVNXrmIK+JXlsksft3QZ+F7iDKfhOzmvoRY0hHsArgX9nNJ/7tqHrOYT6Lwe+C/yM0XzlOYzmYz8P3ANcDzyxe20YHW31LeB2YN3Q9R+gby9mNH/7deC27vHKaegf8Czgq13f7gDe3rWfCNwEbAc+Bjymaz+y29/ePX/i0H0Ys58vBT4zLX3r+vC17nHn3t8Z0/CdnO/hZS4kSb1ZnD6SJC3AUJAk9QwFSVLPUJAk9QwFSVLPUJAk9QwFSVLv/wGCl7PmuvNY4gAAAABJRU5ErkJggg==\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Analise univariavel\n", + "for coluna in colunas_numericas:\n", + " print(coluna)\n", + " df[coluna].plot.hist(bins = 50, log= True)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Analisando a correlacao entre as variaveis númericas\n", + "plt.figure(figsize = (20,20))\n", + "sns.heatmap(df[colunas_numericas].corr().round(2), annot= True)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "correlacionadas = ['GarageArea', 'GarageCars', 'GrLivArea', 'OverallQual']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analisando as features com yellowbrick" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting yellowbrick\n", + " Downloading yellowbrick-1.1-py3-none-any.whl (263 kB)\n", + "\u001b[K |████████████████████████████████| 263 kB 969 kB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: cycler>=0.10.0 in /Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages (from yellowbrick) (0.10.0)\n", + "Requirement already satisfied: scipy>=1.0.0 in /Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages (from yellowbrick) (1.4.1)\n", + "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.2 in /Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages (from yellowbrick) (3.2.1)\n", + "Requirement already satisfied: numpy>=1.13.0 in /Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages (from yellowbrick) (1.18.2)\n", + "Requirement already satisfied: scikit-learn>=0.20 in /Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages (from yellowbrick) (0.22.2.post1)\n", + "Requirement already satisfied: six in /Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages (from cycler>=0.10.0->yellowbrick) (1.14.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2.4.6)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2.8.1)\n", + "Requirement already satisfied: joblib>=0.11 in /Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages (from scikit-learn>=0.20->yellowbrick) (0.14.1)\n", + "Installing collected packages: yellowbrick\n", + "Successfully installed yellowbrick-1.1\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install yellowbrick" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Removendo a coluna ID\n", + "colunas_numericas.remove('Id')\n", + "df = df[colunas_numericas]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_train = df['SalePrice']\n", + "X_train = df.drop(columns = 'SalePrice')" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.classification module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n", + " warnings.warn(message, FutureWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from yellowbrick.features import Rank1D\n", + "\n", + "visualizer = Rank1D(algorithm='shapiro')\n", + "\n", + "visualizer.fit(X_train, y_train) \n", + "visualizer.transform(X_train) \n", + "visualizer.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from yellowbrick.features import PCA\n", + "\n", + "\n", + "visualizer = PCA(scale=True, proj_features=True, projection=2)\n", + "visualizer.fit_transform(X_train[correlacionadas], y_train)\n", + "visualizer.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages/sklearn/base.py:193: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n", + " warnings.warn('From version 0.24, get_params will raise an '\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from yellowbrick.target import FeatureCorrelation\n", + "\n", + "features = list(X_train.columns)\n", + "\n", + "visualizer = FeatureCorrelation(labels=features)\n", + "\n", + "visualizer.fit(X_train, y_train) \n", + "visualizer.show() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Treinando o modelo" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting sklearn\n", + " Using cached sklearn-0.0.tar.gz (1.1 kB)\n", + "Collecting scikit-learn\n", + " Downloading scikit_learn-0.22.2.post1-cp38-cp38-macosx_10_9_x86_64.whl (7.2 MB)\n", + "\u001b[K |████████████████████████████████| 7.2 MB 6.5 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: scipy>=0.17.0 in /Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages (from scikit-learn->sklearn) (1.4.1)\n", + "Requirement already satisfied: numpy>=1.11.0 in /Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages (from scikit-learn->sklearn) (1.18.2)\n", + "Collecting joblib>=0.11\n", + " Using cached joblib-0.14.1-py2.py3-none-any.whl (294 kB)\n", + "Building wheels for collected packages: sklearn\n", + " Building wheel for sklearn (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for sklearn: filename=sklearn-0.0-py2.py3-none-any.whl size=1315 sha256=a06c24060e74cb4e85f9e51f5e3f55cf62c260de007fc4aedc7c860cdc0e3012\n", + " Stored in directory: /Users/tuliosouza/Library/Caches/pip/wheels/22/0b/40/fd3f795caaa1fb4c6cb738bc1f56100be1e57da95849bfc897\n", + "Successfully built sklearn\n", + "Installing collected packages: joblib, scikit-learn, sklearn\n", + "Successfully installed joblib-0.14.1 scikit-learn-0.22.2.post1 sklearn-0.0\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install sklearn" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "reg= LinearRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reg.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "colunas_treinamento = X_train.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "X_test = pd.read_csv('test.csv')\n", + "y_test = pd.read_csv('sample_submission.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "y_test = y_test['SalePrice']" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "X_test= X_test[colunas_treinamento].fillna(df[colunas_treinamento].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = reg.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_squared_error" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "erro_normal = mean_squared_error(y_pred=y_pred, y_true=y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4647194215.33722" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "erro_normal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aplicando o Feature Selection" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_selection import RFE" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "rfe = RFE(reg)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RFE(estimator=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n", + " normalize=False),\n", + " n_features_to_select=None, step=1, verbose=0)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfe.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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colunaboolcoeficientes
0MSSubClassTrue-162.672852
1LotAreaFalse0.396228
2OverallQualTrue17905.067194
3OverallCondTrue4418.794796
4YearBuiltTrue346.653503
5YearRemodAddTrue137.073924
6BsmtFinSF1False11.833598
7BsmtFinSF2False-2.728260
8BsmtUnfSFFalse0.787735
9TotalBsmtSFFalse9.893072
101stFlrSFFalse18.837707
112ndFlrSFFalse18.946369
12LowQualFinSFFalse-6.000309
13GrLivAreaFalse31.783767
14BsmtFullBathTrue8534.894057
15BsmtHalfBathTrue2467.200539
16FullBathTrue3577.489051
17HalfBathTrue-1326.861626
18BedroomAbvGrTrue-10530.779326
19KitchenAbvGrTrue-12927.769856
20TotRmsAbvGrdTrue5132.318055
21FireplacesTrue3596.895112
22GarageCarsTrue10633.749904
23GarageAreaFalse1.396213
24WoodDeckSFFalse26.372691
25OpenPorchSFFalse-5.619397
26EnclosedPorchFalse8.722010
273SsnPorchFalse18.771384
28ScreenPorchFalse57.885991
29PoolAreaFalse-42.613687
30MiscValFalse-0.891248
31MoSoldTrue-115.348621
32YrSoldTrue-757.643913
\n", + "
" + ], + "text/plain": [ + " coluna bool coeficientes\n", + "0 MSSubClass True -162.672852\n", + "1 LotArea False 0.396228\n", + "2 OverallQual True 17905.067194\n", + "3 OverallCond True 4418.794796\n", + "4 YearBuilt True 346.653503\n", + "5 YearRemodAdd True 137.073924\n", + "6 BsmtFinSF1 False 11.833598\n", + "7 BsmtFinSF2 False -2.728260\n", + "8 BsmtUnfSF False 0.787735\n", + "9 TotalBsmtSF False 9.893072\n", + "10 1stFlrSF False 18.837707\n", + "11 2ndFlrSF False 18.946369\n", + "12 LowQualFinSF False -6.000309\n", + "13 GrLivArea False 31.783767\n", + "14 BsmtFullBath True 8534.894057\n", + "15 BsmtHalfBath True 2467.200539\n", + "16 FullBath True 3577.489051\n", + "17 HalfBath True -1326.861626\n", + "18 BedroomAbvGr True -10530.779326\n", + "19 KitchenAbvGr True -12927.769856\n", + "20 TotRmsAbvGrd True 5132.318055\n", + "21 Fireplaces True 3596.895112\n", + "22 GarageCars True 10633.749904\n", + "23 GarageArea False 1.396213\n", + "24 WoodDeckSF False 26.372691\n", + "25 OpenPorchSF False -5.619397\n", + "26 EnclosedPorch False 8.722010\n", + "27 3SsnPorch False 18.771384\n", + "28 ScreenPorch False 57.885991\n", + "29 PoolArea False -42.613687\n", + "30 MiscVal False -0.891248\n", + "31 MoSold True -115.348621\n", + "32 YrSold True -757.643913" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame({'coluna':X_train.columns,\n", + " 'bool': rfe.get_support(),\n", + " 'coeficientes': pd.Series(reg.coef_)})" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_importante = rfe.transform(X_train)\n", + "X_test_importante = rfe.transform(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reg.fit(X_train_importante, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_imp = reg.predict(X_test_importante)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "erro_imp = mean_squared_error(y_pred=y_pred_imp, y_true=y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4628516097.925274" + ] + }, + "execution_count": 158, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "erro_imp" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages/sklearn/base.py:193: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n", + " warnings.warn('From version 0.24, get_params will raise an '\n", + "/Users/tuliosouza/opt/anaconda3/envs/aceleradev_ds/lib/python3.8/site-packages/sklearn/base.py:193: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n", + " warnings.warn('From version 0.24, get_params will raise an '\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from yellowbrick.model_selection import RFECV\n", + "\n", + "\n", + "# Instantiate RFECV visualizer with a linear SVM classifier\n", + "visualizer = RFECV(reg)\n", + "\n", + "visualizer.fit(X_train, y_train) # Fit the data to the visualizer\n", + "visualizer.show() # Finalize and render the figure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aplicando PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA(0.95)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=0.95, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pca.fit(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.98511677])" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pca.explained_variance_ratio_" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_pca = pca.transform(X_train)\n", + "X_test_pca = pca.transform(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reg = LinearRegression()\n", + "reg.fit(X_train_pca, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_pca = reg.predict(X_test_pca)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "erro_pca = mean_squared_error(y_pred=y_pred_pca, y_true=y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.DataFrame({'erro' : [erro_normal, erro_imp, erro_pca]}).plot(kind = 'bar', log = True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Semana 7/.ipynb_checkpoints/aula_7_feature_engineering-checkpoint.ipynb b/Semana 7/.ipynb_checkpoints/aula_7_feature_engineering-checkpoint.ipynb new file mode 100644 index 0000000..4c91e8d --- /dev/null +++ b/Semana 7/.ipynb_checkpoints/aula_7_feature_engineering-checkpoint.ipynb @@ -0,0 +1,5350 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MyaSGq65woLh" + }, + "source": [ + "![Codenation](https://forum.codenation.com.br/uploads/default/original/2X/2/2d2d2a9469f0171e7df2c4ee97f70c555e431e76.png)\n", + "\n", + "__Autor__: Kazuki Yokoyama (kazuki.yokoyama@ufrgs.br)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mi4xZxcfBA2U" + }, + "source": [ + "# _Feature engineering_\n", + "\n", + "![cover](https://venturebeat.com/wp-content/uploads/2018/07/feature_engineering.jpg?resize=680%2C198&strip=all)\n", + "\n", + "Neste módulo, trabalharemos a engenharia de _features_, que consiste em preparar os nossos dados para alimentar os algoritmos de ML adequadamente. Ao contrário do mundo dos tutoriais, na vida real os dados dificilmente estarão prontos para serem consumidos. Grande parte do tempo de um projeto de ML é gasto com a engenharia de _features_, e quanto melhor a qualidade desta etapa, maiores são as chances de melhores resultados nas etapas seguintes." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "cAxxSlo3QrZV" + }, + "source": [ + "## Importação das bibliotecas" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "jMxYy1NkQwW6" + }, + "outputs": [], + "source": [ + "import functools\n", + "from math import sqrt\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import statsmodels.api as sm\n", + "import scipy.stats as sct\n", + "import seaborn as sns\n", + "from sklearn.datasets import load_digits, fetch_20newsgroups\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.feature_extraction.text import (\n", + " CountVectorizer, TfidfTransformer, TfidfVectorizer\n", + ")\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import (\n", + " OneHotEncoder, Binarizer, KBinsDiscretizer,\n", + " MinMaxScaler, StandardScaler, PolynomialFeatures\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "xNbPRHkKQyv2" + }, + "outputs": [], + "source": [ + "# Algumas configurações para o matplotlib.\n", + "%matplotlib inline\n", + "\n", + "from IPython.core.pylabtools import figsize\n", + "\n", + "\n", + "figsize(12, 12)\n", + "\n", + "sns.set()" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "m8onCO86Q2Hm" + }, + "outputs": [], + "source": [ + "np.random.seed(1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "EIEVdatWDh3Z" + }, + "source": [ + "## _One-hot encoding_\n", + "\n", + "Até aqui, nós praticamente ignoramos a existência de variáveis categóricas. Focamos nas variáveis numéricas porque elas são simples de lidar e bastante comuns. Ainda assim, variáveis categóricas são encontradas facilmente e precisamos de uma forma de trabalhar com elas.\n", + "\n", + "Uma das formas mais simples de representação de variáveis categóricas é através do método chamado _one-hot enconding_. Com ele, uma variável categórica com $h$ categorias é transformada em $h$ novas variáveis binárias (0 ou 1), onde a presença do 1 (_hot_) significa que aquela observação pertence àquela categoria, e 0 (_cold_) que não pertence. Veja um exemplo abaixo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "M1zv6xPDk4ym", + "outputId": "b9b41a48-556d-44e1-f142-708bae7a2d02" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourse
01.5396.61Biology
11.7646.42Biology
21.6958.95Biology
31.8295.14Biology
41.6406.43Physics
51.7787.98Physics
61.6797.90Biology
71.6046.76Physics
81.8197.44Physics
91.6076.01Physics
\n", + "
" + ], + "text/plain": [ + " Height Score Course\n", + "0 1.539 6.61 Biology\n", + "1 1.764 6.42 Biology\n", + "2 1.695 8.95 Biology\n", + "3 1.829 5.14 Biology\n", + "4 1.640 6.43 Physics\n", + "5 1.778 7.98 Physics\n", + "6 1.679 7.90 Biology\n", + "7 1.604 6.76 Physics\n", + "8 1.819 7.44 Physics\n", + "9 1.607 6.01 Physics" + ] + }, + "execution_count": 4, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "rows = 100\n", + "\n", + "height = np.random.normal(loc=1.70, scale=0.2, size=rows).round(3)\n", + "score = np.random.normal(loc=7, scale=1, size=rows).round(2)\n", + "courses = [\"Math\", \"Physics\", \"Biology\"]\n", + "course = np.random.choice(courses, size=rows)\n", + "\n", + "data = pd.DataFrame({\"Height\": height, \"Score\": score, \"Course\": course})\n", + "\n", + "data.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nK_6LysZP6Lw" + }, + "source": [ + "Criamos um _data set_ que contém duas variáveis numéricas (`Height` e `Score`) e uma variável categórica (`Course`). Nosso objetivo com o _one-hot encoding_ é transformar a variável `Course` em uma sequência de variáveis numéricas binárias, cada uma descrevendo uma classe da variável. Neste caso, como temos três categorias para `Course` (Biology, Physics e Math), teremos três novas variáveis binárias.\n", + "\n", + "Vamos treinar esse _encoder_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "MDpY6XcNmYlw", + "outputId": "5fda81c9-000d-4557-cb3f-22d012b3e548" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 0, 0],\n", + " [1, 0, 0],\n", + " [1, 0, 0],\n", + " [1, 0, 0],\n", + " [0, 0, 1],\n", + " [0, 0, 1],\n", + " [1, 0, 0],\n", + " [0, 0, 1],\n", + " [0, 0, 1],\n", + " [0, 0, 1]])" + ] + }, + "execution_count": 5, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "one_hot_encoder = OneHotEncoder(sparse=False, dtype=np.int)\n", + "\n", + "#one_hot_encoder.fit(data[[\"Course\"]])\n", + "\n", + "#course_encoded = one_hot_encoder.transform(...)\n", + "\n", + "course_encoded = one_hot_encoder.fit_transform(data[[\"Course\"]])\n", + "\n", + "course_encoded[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "V-O0cMCyQqk4" + }, + "source": [ + "A saída é um `np.ndarray` com formato `(n, h)`, onde `n` é o número de observações no _data set_ e `h` é o número de categorias da variável codificada." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "BP_QsDI6REl_", + "outputId": "10a0faf0-b05f-4ad8-f79d-7642d15862a7" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(100, 3)" + ] + }, + "execution_count": 6, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "course_encoded.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eoRT2AR8RHNl" + }, + "source": [ + "No atributo `categories_` do _encoder_, temos as categorias da variável:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "ziGE3VCinqM7", + "outputId": "2c77ac8b-ba1b-4479-97aa-b59cff8b78bf" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[array(['Biology', 'Math', 'Physics'], dtype=object)]" + ] + }, + "execution_count": 7, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "one_hot_encoder.categories_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "y8V2WMjmRUkw" + }, + "source": [ + "Podemos criar as novas colunas que descrevem cada categoria. Repare que, para qualquer linha, apenas uma das colunas contém um 1, indicando a qual categoria aquela observação pertence. Isso acontece, obviamente, se as categorias forem mutuamente exclusivas (uma observação não pode pertencer a mais de uma categoria simultaneamente)." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "dGepWPRFoqc0", + "outputId": "dc6a6dff-007d-4f66-cbfb-2aad4c8a7448" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
HeightScoreCourseBiologyMathPhysics
01.5396.61Biology100
11.7646.42Biology100
21.6958.95Biology100
31.8295.14Biology100
41.6406.43Physics001
51.7787.98Physics001
61.6797.90Biology100
71.6046.76Physics001
81.8197.44Physics001
91.6076.01Physics001
\n", + "
" + ], + "text/plain": [ + " Height Score Course Biology Math Physics\n", + "0 1.539 6.61 Biology 1 0 0\n", + "1 1.764 6.42 Biology 1 0 0\n", + "2 1.695 8.95 Biology 1 0 0\n", + "3 1.829 5.14 Biology 1 0 0\n", + "4 1.640 6.43 Physics 0 0 1\n", + "5 1.778 7.98 Physics 0 0 1\n", + "6 1.679 7.90 Biology 1 0 0\n", + "7 1.604 6.76 Physics 0 0 1\n", + "8 1.819 7.44 Physics 0 0 1\n", + "9 1.607 6.01 Physics 0 0 1" + ] + }, + "execution_count": 8, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "columns_encoded = one_hot_encoder.categories_[0]\n", + "\n", + "data_encoded = pd.concat([data, pd.DataFrame(course_encoded, columns=columns_encoded)], axis=1)\n", + "\n", + "data_encoded.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iIiVR7P4SHXz" + }, + "source": [ + "Como você deve imaginar, a maior parte da matriz retornada é composta por zeros, sendo apenas alguns elementos compostos de um. Dizemos que essa matriz é __esparsa__. É um grande desperdício de memória trabalhar diretamente como uma matriz esparsa assim. Por isso, o _default_ do `OneHotEncoder` é retornar uma `sparse matrix` do NumPy, economizando espaço em memória:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "colab_type": "code", + "id": "muGSmJckraf3", + "outputId": "c8957d2b-68c4-4722-80ea-5e241c479a88" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<100x3 sparse matrix of type ''\n", + "\twith 100 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 9, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "one_hot_encoder_sparse = OneHotEncoder(sparse=True) # sparse=True é o default.\n", + "\n", + "course_encoded_sparse = one_hot_encoder_sparse.fit_transform(data[[\"Course\"]])\n", + "\n", + "course_encoded_sparse" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "FOYl0Lx8TPJm" + }, + "source": [ + "Para acessar os dados dessa matriz, podemos convertê-la para um _array_ não esparso:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "mtUziaQmrqTN", + "outputId": "bb7920ae-69a0-4543-97da-b1fc2746ddd0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [1., 0., 0.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.]])" + ] + }, + "execution_count": 10, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "course_encoded_sparse.toarray()[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "zHGmVXu1uEvM" + }, + "source": [ + "## Binarização (_Binarization_)\n", + "\n", + "Binarização é o processo de discretizar uma variável numérica em dois níveis com base em um _threshold_. Isso pode ser útil, por exemplo, para tornar uma variável numérica contínua em uma variável binária alvo de duas classes (positiva ou negativa).\n", + "\n", + "No exemplo abaixo, vamos separar a variável `Height` em dois grupos, utilizando 1.80 m como _threshold_ de separação. Observações que possuam menos de 1.80 m terão valor 0, enquanto aquelas com mais de 1.80 m terão valor 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 208 + }, + "colab_type": "code", + "id": "PeGrPpyWPcOw", + "outputId": "edb6b4c4-97e9-4914-f952-aa60c6dbbbc2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 True\n", + "4 False\n", + "5 False\n", + "6 False\n", + "7 False\n", + "8 True\n", + "9 False\n", + "Name: Height, dtype: bool" + ] + }, + "execution_count": 11, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "tall = (data_encoded.Height > 1.80)\n", + "\n", + "tall[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "94vcsMVguGvG", + "outputId": "b2b15447-7399-4309-b18a-3de5a183a41e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.],\n", + " [0.],\n", + " [0.],\n", + " [1.],\n", + " [0.],\n", + " [0.],\n", + " [0.],\n", + " [0.],\n", + " [1.],\n", + " [0.]])" + ] + }, + "execution_count": 12, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "binarizer = Binarizer(threshold=1.80).fit(data_encoded[[\"Height\"]])\n", + "\n", + "height_binary = binarizer.transform(data_encoded[[\"Height\"]])\n", + "\n", + "height_binary[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "oND_xnxRV8wZ" + }, + "source": [ + "O `Binarizer` tem como saída uma matriz binária numérica. Podemos transformá-la em um vetor de _bool_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "iXbf50-4vdDR", + "outputId": "2f7dba40-f513-491a-e072-743ac0a8c88f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Tall\n", + "0 False\n", + "1 False\n", + "2 False\n", + "3 True\n", + "4 False\n", + "5 False\n", + "6 False\n", + "7 False\n", + "8 True\n", + "9 False" + ] + }, + "execution_count": 13, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "height_bool = pd.DataFrame(height_binary.flatten().astype(bool), columns=[\"Tall\"])\n", + "\n", + "height_bool.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nn9Gs9DhWNvi" + }, + "source": [ + "Vamos adicionar a nova variável `Tall`, que indica se a pessoa é alta (> 1.80 m), ao nosso _data set_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "xjOV0WlJy7DY", + "outputId": "af316c4b-4931-44cb-a4af-4fa51b3c93fc" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourseBiologyMathPhysicsTall
01.5396.61Biology100False
11.7646.42Biology100False
21.6958.95Biology100False
31.8295.14Biology100True
41.6406.43Physics001False
51.7787.98Physics001False
61.6797.90Biology100False
71.6046.76Physics001False
81.8197.44Physics001True
91.6076.01Physics001False
\n", + "
" + ], + "text/plain": [ + " Height Score Course Biology Math Physics Tall\n", + "0 1.539 6.61 Biology 1 0 0 False\n", + "1 1.764 6.42 Biology 1 0 0 False\n", + "2 1.695 8.95 Biology 1 0 0 False\n", + "3 1.829 5.14 Biology 1 0 0 True\n", + "4 1.640 6.43 Physics 0 0 1 False\n", + "5 1.778 7.98 Physics 0 0 1 False\n", + "6 1.679 7.90 Biology 1 0 0 False\n", + "7 1.604 6.76 Physics 0 0 1 False\n", + "8 1.819 7.44 Physics 0 0 1 True\n", + "9 1.607 6.01 Physics 0 0 1 False" + ] + }, + "execution_count": 14, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_encoded = pd.concat([data_encoded, height_bool], axis=1)\n", + "\n", + "data_encoded.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2tOdmnNi23p4" + }, + "source": [ + "## Discretização (_Binning_)\n", + "\n", + "Discretização, como o nome diz, é o processo de discretizar ou separar em intervalos contínuos uma variável numérica. Isso pode ser útil para converter uma variável numérica em categórica, quando o valor exato numérico não for tão importante quanto o intervalo onde ele se encontra.\n", + "\n", + "Podemos criar _bins_ (_buckets_ ou intervalos) que contenham aproximadamente a mesma quantidade de observações, utilizando a estratégia `quantile` ou que sejam igualmente espaçados com a estratégia `uniform`.\n", + "\n", + "No exemplo a seguir, criamos quatro intervalos da variável `Score` com a estratégia `quantile`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "Xir4K6i522ZQ", + "outputId": "e902850a-d3dc-4d97-a80f-ad3dad1bb1a2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.],\n", + " [1.],\n", + " [3.],\n", + " [0.],\n", + " [1.],\n", + " [3.],\n", + " [3.],\n", + " [2.],\n", + " [2.],\n", + " [0.]])" + ] + }, + "execution_count": 15, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "discretizer = KBinsDiscretizer(n_bins=4, encode=\"ordinal\", strategy=\"quantile\")\n", + "\n", + "discretizer.fit(data_encoded[[\"Score\"]])\n", + "\n", + "score_bins = discretizer.transform(data_encoded[[\"Score\"]])\n", + "\n", + "score_bins[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3hrP6E4xYXCs" + }, + "source": [ + "Os limites dos intervalos estão disponíveis no atributo `bin_edges_`. Isso pode ser útil para criarmos _labels_ para colunas do _data set_ por exemplo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "ScCmeNtn3-fF", + "outputId": "be1003a5-2d28-42d6-e76d-bc349e957e95" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([array([4.09 , 6.1975, 6.735 , 7.6 , 9.28 ])], dtype=object)" + ] + }, + "execution_count": 16, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "discretizer.bin_edges_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vGl5ONq2Yk7r" + }, + "source": [ + "A função `get_interval()` abaixo facilita a criação de _labels_ indicativas dos intervalos:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "fvB70_vd4fSO" + }, + "outputs": [], + "source": [ + "def get_interval(bin_idx, bin_edges):\n", + " return f\"{np.round(bin_edges[bin_idx], 2):.2f} ⊢ {np.round(bin_edges[bin_idx+1], 2):.2f}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Hn3eqHFbYtfm" + }, + "source": [ + "Cada um dos intervalos mostrados abaixo deve possuir aproximadamente a mesma quantidade de observações:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "colab_type": "code", + "id": "HX59pepN5ZQQ", + "outputId": "d5b3d4dc-c969-44cb-fa34-e31fad2dd818" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bins quantile\n", + "interval: #elements\n", + "\n", + "4.09 ⊢ 6.20: 25\n", + "6.20 ⊢ 6.74: 25\n", + "6.74 ⊢ 7.60: 25\n", + "7.60 ⊢ 9.28: 25\n" + ] + } + ], + "source": [ + "bin_edges_quantile = discretizer.bin_edges_[0]\n", + "\n", + "print(f\"Bins quantile\")\n", + "print(f\"interval: #elements\\n\")\n", + "for i in range(len(discretizer.bin_edges_[0])-1):\n", + " print(f\"{get_interval(i, bin_edges_quantile)}: {sum(score_bins[:, 0] == i)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OQ0fli3IY2G6" + }, + "source": [ + "A _Series_ abaixo mostra alguns dos intervalos para os quais as observações foram encaixadas:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 208 + }, + "colab_type": "code", + "id": "SZMBYjqR5-H6", + "outputId": "cba541dc-9f9e-48d8-eb87-fa54440ca353" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 6.20 ⊢ 6.74\n", + "1 6.20 ⊢ 6.74\n", + "2 7.60 ⊢ 9.28\n", + "3 4.09 ⊢ 6.20\n", + "4 6.20 ⊢ 6.74\n", + "5 7.60 ⊢ 9.28\n", + "6 7.60 ⊢ 9.28\n", + "7 6.74 ⊢ 7.60\n", + "8 6.74 ⊢ 7.60\n", + "9 4.09 ⊢ 6.20\n", + "dtype: object" + ] + }, + "execution_count": 19, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "score_intervals = pd.Series(score_bins.flatten().astype(np.int)).apply(get_interval, args=(bin_edges_quantile,))\n", + "\n", + "score_intervals.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6gWE7IU6Y_9q" + }, + "source": [ + "Também podemos criar uma nova variável, `Score_interval`, no nosso _data set_ com os intervalos (que agora são categorias):" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "fomFOQbVA8eS", + "outputId": "1f065c4f-6da4-43ad-ebb7-b58706595871" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourseBiologyMathPhysicsTallScore_interval
01.5396.61Biology100False6.20 ⊢ 6.74
11.7646.42Biology100False6.20 ⊢ 6.74
21.6958.95Biology100False7.60 ⊢ 9.28
31.8295.14Biology100True4.09 ⊢ 6.20
41.6406.43Physics001False6.20 ⊢ 6.74
51.7787.98Physics001False7.60 ⊢ 9.28
61.6797.90Biology100False7.60 ⊢ 9.28
71.6046.76Physics001False6.74 ⊢ 7.60
81.8197.44Physics001True6.74 ⊢ 7.60
91.6076.01Physics001False4.09 ⊢ 6.20
\n", + "
" + ], + "text/plain": [ + " Height Score Course Biology Math Physics Tall Score_interval\n", + "0 1.539 6.61 Biology 1 0 0 False 6.20 ⊢ 6.74\n", + "1 1.764 6.42 Biology 1 0 0 False 6.20 ⊢ 6.74\n", + "2 1.695 8.95 Biology 1 0 0 False 7.60 ⊢ 9.28\n", + "3 1.829 5.14 Biology 1 0 0 True 4.09 ⊢ 6.20\n", + "4 1.640 6.43 Physics 0 0 1 False 6.20 ⊢ 6.74\n", + "5 1.778 7.98 Physics 0 0 1 False 7.60 ⊢ 9.28\n", + "6 1.679 7.90 Biology 1 0 0 False 7.60 ⊢ 9.28\n", + "7 1.604 6.76 Physics 0 0 1 False 6.74 ⊢ 7.60\n", + "8 1.819 7.44 Physics 0 0 1 True 6.74 ⊢ 7.60\n", + "9 1.607 6.01 Physics 0 0 1 False 4.09 ⊢ 6.20" + ] + }, + "execution_count": 20, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_encoded = pd.concat([data_encoded, pd.DataFrame(score_intervals, columns=[\"Score_interval\"])], axis=1)\n", + "\n", + "data_encoded.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LldlZ92lZN1k" + }, + "source": [ + "Como dito, podemos utilizar a estratégia `uniform` para criar _bins_ igualmente espaçados, independente do número de observações que cada um possui. Também podemos especificar o tipo de codificação utilizada. No caso a seguir, utilizamos `encode=onehot-dense` para informar que queremos que a saída seja codificada como o _one-hot encode_ visto anteriormente:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "E6L1qXuW-v-n", + "outputId": "956f9e9f-67ba-436f-f457-889ee2d1f3db" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 0, 0],\n", + " [0, 1, 0, 0],\n", + " [0, 0, 0, 1],\n", + " [1, 0, 0, 0],\n", + " [0, 1, 0, 0],\n", + " [0, 0, 1, 0],\n", + " [0, 0, 1, 0],\n", + " [0, 0, 1, 0],\n", + " [0, 0, 1, 0],\n", + " [0, 1, 0, 0]])" + ] + }, + "execution_count": 21, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "discretizer_uniform = KBinsDiscretizer(n_bins=4, encode=\"onehot-dense\", strategy=\"uniform\")\n", + "\n", + "discretizer_uniform.fit(data_encoded[[\"Score\"]])\n", + "\n", + "score_bins_uniform = discretizer_uniform.transform(data_encoded[[\"Score\"]]).astype(np.int)\n", + "\n", + "score_bins_uniform[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "YapI8RuMZZfM" + }, + "source": [ + "Note como agora os intervalos são ligeiramente diferentes:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "P8gW9k-w-_CC", + "outputId": "731fca86-f052-4a93-e5bf-e13eec18ac8b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4.09 , 5.3875, 6.685 , 7.9825, 9.28 ])" + ] + }, + "execution_count": 22, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "bin_edges_uniform = discretizer_uniform.bin_edges_[0]\n", + "\n", + "bin_edges_uniform" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "colab_type": "code", + "id": "ieyy46EJAnb6", + "outputId": "99835fa9-8003-4060-afae-2c4de66685ff" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bins uniform\n", + "interval: #elements\n", + "\n", + "4.09 ⊢ 5.39: 6\n", + "5.39 ⊢ 6.68: 43\n", + "6.68 ⊢ 7.98: 44\n", + "7.98 ⊢ 9.28: 7\n" + ] + } + ], + "source": [ + "score_intervals_columns = [get_interval(i, bin_edges_uniform) for i in range(4)]\n", + "\n", + "print(f\"Bins uniform\")\n", + "print(f\"interval: #elements\\n\")\n", + "for i in range(len(discretizer_uniform.bin_edges_[0])-1):\n", + " print(f\"{get_interval(i, bin_edges_uniform)}: {sum(score_bins_uniform[:, i])}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "WuWi-1U4Zzf_" + }, + "source": [ + "Podemos adicionar as novas variáveis binárias no _data set_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "P-v3UgiQB87S", + "outputId": "ad22d68f-c0e8-4a91-8838-842e7e2f5041" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourseBiologyMathPhysicsTallScore_interval4.09 ⊢ 5.395.39 ⊢ 6.686.68 ⊢ 7.987.98 ⊢ 9.28
01.5396.61Biology100False6.20 ⊢ 6.740100
11.7646.42Biology100False6.20 ⊢ 6.740100
21.6958.95Biology100False7.60 ⊢ 9.280001
31.8295.14Biology100True4.09 ⊢ 6.201000
41.6406.43Physics001False6.20 ⊢ 6.740100
51.7787.98Physics001False7.60 ⊢ 9.280010
61.6797.90Biology100False7.60 ⊢ 9.280010
71.6046.76Physics001False6.74 ⊢ 7.600010
81.8197.44Physics001True6.74 ⊢ 7.600010
91.6076.01Physics001False4.09 ⊢ 6.200100
\n", + "
" + ], + "text/plain": [ + " Height Score Course ... 5.39 ⊢ 6.68 6.68 ⊢ 7.98 7.98 ⊢ 9.28\n", + "0 1.539 6.61 Biology ... 1 0 0\n", + "1 1.764 6.42 Biology ... 1 0 0\n", + "2 1.695 8.95 Biology ... 0 0 1\n", + "3 1.829 5.14 Biology ... 0 0 0\n", + "4 1.640 6.43 Physics ... 1 0 0\n", + "5 1.778 7.98 Physics ... 0 1 0\n", + "6 1.679 7.90 Biology ... 0 1 0\n", + "7 1.604 6.76 Physics ... 0 1 0\n", + "8 1.819 7.44 Physics ... 0 1 0\n", + "9 1.607 6.01 Physics ... 1 0 0\n", + "\n", + "[10 rows x 12 columns]" + ] + }, + "execution_count": 24, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_encoded = pd.concat([data_encoded, pd.DataFrame(score_bins_uniform, columns=score_intervals_columns)], axis=1)\n", + "\n", + "data_encoded.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jD8WM_-yzqSc" + }, + "source": [ + "## Normalização (_Scaling_)\n", + "\n", + "Normalização é o processo de colocar uma variável numérica em uma escala pré-determinada, geralmente $[0, 1]$, mas também é comum ser $[-1, 1]$.\n", + "\n", + "Para colocar no intervalo $[0, 1]$, basta subtrair cada valor da valor mínimo e dividir pela diferença do valor máximo e mínimo:\n", + "\n", + "$$x_{\\text{scaled}} = \\frac{x - x_{\\text{min}}}{x_{\\text{max}} - x_{\\text{min}}}$$\n", + "\n", + "Abaixo, escalamos a variável `Score` no intervalo $[0, 1]$:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "nMM2mu-Qzwnv", + "outputId": "5c60c83b-13bf-431d-e77e-a2fb2e8af317" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.48554913],\n", + " [0.44894027],\n", + " [0.93641618],\n", + " [0.20231214],\n", + " [0.45086705],\n", + " [0.7495183 ],\n", + " [0.73410405],\n", + " [0.51445087],\n", + " [0.64547206],\n", + " [0.3699422 ]])" + ] + }, + "execution_count": 25, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "minmax_scaler = MinMaxScaler(feature_range=(0, 1)) # Default feature_scale é (0, 1).\n", + "\n", + "minmax_scaler.fit(data_encoded[[\"Score\"]])\n", + "\n", + "score_normalized = minmax_scaler.transform(data_encoded[[\"Score\"]])\n", + "\n", + "score_normalized[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "FPr-37M2UBj4", + "outputId": "dc170301-56af-4cab-da7c-307c5cbb94a6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 0.9999999999999999)" + ] + }, + "execution_count": 26, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "score_normalized.min(), score_normalized.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Et6m_2Bbbq-n" + }, + "source": [ + "Adicionamos a variável `Score` normalizada ao nosso _data set_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "kaYvCQtK0fzi", + "outputId": "9f8ccb6c-d0b7-4445-96c9-490f284f2357" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourseBiologyMathPhysicsTallScore_interval4.09 ⊢ 5.395.39 ⊢ 6.686.68 ⊢ 7.987.98 ⊢ 9.28Score_normalized
01.5396.61Biology100False6.20 ⊢ 6.7401000.485549
11.7646.42Biology100False6.20 ⊢ 6.7401000.448940
21.6958.95Biology100False7.60 ⊢ 9.2800010.936416
31.8295.14Biology100True4.09 ⊢ 6.2010000.202312
41.6406.43Physics001False6.20 ⊢ 6.7401000.450867
51.7787.98Physics001False7.60 ⊢ 9.2800100.749518
61.6797.90Biology100False7.60 ⊢ 9.2800100.734104
71.6046.76Physics001False6.74 ⊢ 7.6000100.514451
81.8197.44Physics001True6.74 ⊢ 7.6000100.645472
91.6076.01Physics001False4.09 ⊢ 6.2001000.369942
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" + ], + "text/plain": [ + " Height Score Course ... 6.68 ⊢ 7.98 7.98 ⊢ 9.28 Score_normalized\n", + "0 1.539 6.61 Biology ... 0 0 0.485549\n", + "1 1.764 6.42 Biology ... 0 0 0.448940\n", + "2 1.695 8.95 Biology ... 0 1 0.936416\n", + "3 1.829 5.14 Biology ... 0 0 0.202312\n", + "4 1.640 6.43 Physics ... 0 0 0.450867\n", + "5 1.778 7.98 Physics ... 1 0 0.749518\n", + "6 1.679 7.90 Biology ... 1 0 0.734104\n", + "7 1.604 6.76 Physics ... 1 0 0.514451\n", + "8 1.819 7.44 Physics ... 1 0 0.645472\n", + "9 1.607 6.01 Physics ... 0 0 0.369942\n", + "\n", + "[10 rows x 13 columns]" + ] + }, + "execution_count": 27, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_encoded = pd.concat([data_encoded, pd.DataFrame(score_normalized.flatten(), columns=[\"Score_normalized\"])], axis=1)\n", + "\n", + "data_encoded.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "n7-msElsbveR" + }, + "source": [ + "Para avaliar se os valores encontrados conferem, podemos utilizar a função `normalize` abaixo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "EAfUGaFc061d" + }, + "outputs": [], + "source": [ + "def normalize(x, xmin, xmax):\n", + " return (x - xmin)/(xmax - xmin)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "CXywxNX-b-0K" + }, + "source": [ + "A função `partial()` do módulo `functools` (_builtin_ do Python) permite \"congelar\" alguns parâmetros da função passaga como argumento, facilitando a invocação desta função quando tais parâmetros são constantes. No caso abaixo, \"congelamos\" os argumentos `xmin` e `xmax` da função `normalize()` com os valores mínimo e máximo da variável `Score`, respectivamente. Nas invocações subsequentes de `normalize` não precisaremos passar esses argumentos, somente o argumento \"não congelado\" `x`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "UAlpigp21OVx" + }, + "outputs": [], + "source": [ + "normalize_score = functools.partial(normalize,\n", + " xmin=data_encoded.Score.min(),\n", + " xmax=data_encoded.Score.max())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nhR0rwUIctTa" + }, + "source": [ + "O valor abaixo realmente confere com aquele encontrado pelo `MinMaxScaler`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "pMfk3jrU1mQV", + "outputId": "f9851c0d-9446-4f10-874e-cdba22b43722" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.485549" + ] + }, + "execution_count": 30, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "normalize_score(data_encoded.Score[0]).round(6)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HEcSQzWJ2Yum" + }, + "source": [ + "## Padronização (_Standardization_)\n", + "\n", + "Padronização é o processo de tornar a variável com média zero e variância um. Esse processo não deve ser confundido com a normalização descrita acima.\n", + "\n", + "O processo é simples, basta subtrair a média dos dados de cada observação e dividi-los pelo desvio-padrão:\n", + "\n", + "$$x_{\\text{standardized}} = \\frac{x - \\bar{x}}{s}$$\n", + "\n", + "onde $\\bar{x}$ indica a média amostral e $s$ o desvio-padrão amostral." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kXYXezCNdYue" + }, + "source": [ + "No exemplo abaixo, padronizamos a variável `Score`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "Qfhs3Eaq2dGV", + "outputId": "572aae65-5460-44d1-8134-dbc26f82e2d2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.20752554],\n", + " [-0.40839081],\n", + " [ 2.26628886],\n", + " [-1.76158843],\n", + " [-0.39781896],\n", + " [ 1.24081879],\n", + " [ 1.15624393],\n", + " [-0.0489477 ],\n", + " [ 0.66993854],\n", + " [-0.84183693]])" + ] + }, + "execution_count": 31, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "standard_scaler = StandardScaler()\n", + "\n", + "standard_scaler.fit(data_encoded[[\"Score\"]])\n", + "\n", + "score_standardized = standard_scaler.transform(data_encoded[[\"Score\"]])\n", + "\n", + "score_standardized[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "SJJucIQddgME" + }, + "source": [ + "E adicionamos a variável padronizada ao nosso _data set_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "BAndWLe13RSr", + "outputId": "4a6231c1-f459-4307-ad14-24c4e46760cd" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourseBiologyMathPhysicsTallScore_interval4.09 ⊢ 5.395.39 ⊢ 6.686.68 ⊢ 7.987.98 ⊢ 9.28Score_normalizedScore_standardized
01.5396.61Biology100False6.20 ⊢ 6.7401000.485549-0.207526
11.7646.42Biology100False6.20 ⊢ 6.7401000.448940-0.408391
21.6958.95Biology100False7.60 ⊢ 9.2800010.9364162.266289
31.8295.14Biology100True4.09 ⊢ 6.2010000.202312-1.761588
41.6406.43Physics001False6.20 ⊢ 6.7401000.450867-0.397819
51.7787.98Physics001False7.60 ⊢ 9.2800100.7495181.240819
61.6797.90Biology100False7.60 ⊢ 9.2800100.7341041.156244
71.6046.76Physics001False6.74 ⊢ 7.6000100.514451-0.048948
81.8197.44Physics001True6.74 ⊢ 7.6000100.6454720.669939
91.6076.01Physics001False4.09 ⊢ 6.2001000.369942-0.841837
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" + ], + "text/plain": [ + " Height Score Course ... 7.98 ⊢ 9.28 Score_normalized Score_standardized\n", + "0 1.539 6.61 Biology ... 0 0.485549 -0.207526\n", + "1 1.764 6.42 Biology ... 0 0.448940 -0.408391\n", + "2 1.695 8.95 Biology ... 1 0.936416 2.266289\n", + "3 1.829 5.14 Biology ... 0 0.202312 -1.761588\n", + "4 1.640 6.43 Physics ... 0 0.450867 -0.397819\n", + "5 1.778 7.98 Physics ... 0 0.749518 1.240819\n", + "6 1.679 7.90 Biology ... 0 0.734104 1.156244\n", + "7 1.604 6.76 Physics ... 0 0.514451 -0.048948\n", + "8 1.819 7.44 Physics ... 0 0.645472 0.669939\n", + "9 1.607 6.01 Physics ... 0 0.369942 -0.841837\n", + "\n", + "[10 rows x 14 columns]" + ] + }, + "execution_count": 32, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_encoded = pd.concat([data_encoded, pd.DataFrame(score_standardized.flatten(), columns=[\"Score_standardized\"])], axis=1)\n", + "\n", + "data_encoded.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_SgwGLgOdk5Q" + }, + "source": [ + "Note que, ao contrário da variável normalizada, é possível ter valores negativos e positivos, menores e maiores que um. Isso é bem óbvio, pois os dados agora têm média 0 e variância 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "I0E9fwo93h9w", + "outputId": "2d9d5cdf-181b-4ca1-bea7-b382bf738ebd" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.2501111257279262e-15, 1.0101010101010102)" + ] + }, + "execution_count": 33, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_encoded.Score_standardized.mean(), data_encoded.Score_standardized.var()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Av0cwG_Qd3Ow" + }, + "source": [ + "Novamente, para avaliar os resultados obtidos, podemos escrever nossa própria função de padronização:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "khwEkoks3-cS" + }, + "outputs": [], + "source": [ + "def standardize(x, xmean, xstd):\n", + " return (x - xmean)/xstd" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "14w3018J4Gwy" + }, + "outputs": [], + "source": [ + "standardize_score = functools.partial(standardize,\n", + " xmean=data_encoded.Score.mean(),\n", + " xstd=data_encoded.Score.std())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UAGxoUK5d-22" + }, + "source": [ + "Como esperado, o valor confere com o encontrado:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "dpaNVzOy4aCL", + "outputId": "fa0f42f0-32a5-48f4-f8d7-724350cdca86" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.20648530634442175" + ] + }, + "execution_count": 36, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "standardize_score(data_encoded.Score[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2tO4OOJK7NY1" + }, + "source": [ + "## Criando um _Pipeline_\n", + "\n", + "Todo esse processo de transformar os dados pode ser bastante trabalhoso e entendiante. Para facilitar as coisas, o sklearn dispõe de um mecanismo de _pipeline_ que funciona como ao esteira de uma linha de montagem. Cada etapa desse _pipeline_ é uma transformação nos dados, de forma que, ao final do _pipeline_, temos os dados totalmente transformados. A vantagem é que agora especificamos todas as etapas, ou transformações, de uma só vez, e podemos reaproveitar esse _pipeline_ no futuro." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "T1LyaI0-B2hV", + "outputId": "011176a0-ec92-4122-9fc4-3b3d0a3118c9" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourse
01.5396.61Biology
11.7646.42Biology
21.6958.95Biology
31.8295.14Biology
41.6406.43Physics
51.7787.98Physics
61.6797.90Biology
71.6046.76Physics
81.8197.44Physics
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" + ], + "text/plain": [ + " Height Score Course\n", + "0 1.539 6.61 Biology\n", + "1 1.764 6.42 Biology\n", + "2 1.695 8.95 Biology\n", + "3 1.829 5.14 Biology\n", + "4 1.640 6.43 Physics\n", + "5 1.778 7.98 Physics\n", + "6 1.679 7.90 Biology\n", + "7 1.604 6.76 Physics\n", + "8 1.819 7.44 Physics\n", + "9 1.607 6.01 Physics" + ] + }, + "execution_count": 37, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "86on9pLMeidf" + }, + "source": [ + "Para evitar bagunçar com nosso _data set_ original, criamos uma cópia (rasa) dele:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "vdA8euCcZeq1" + }, + "outputs": [], + "source": [ + "data_missing = data.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "snDUyWqEenh8" + }, + "source": [ + "E para tornar o exemplo mais interessante, adicionamos (ou removemos?) dados faltantes ao _data set_. Isso porque uma das transformações úteis que podemos aplicar no _pipeline_ é justamente a imputação de dados, ou seja, preencher dados faltantes.\n", + "\n", + "As variáveis numéricas faltantes são representadas por `np.nan`, enquanto a variável categórica é representada pela classe `Unknown`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "LkVnbFAKS_fF", + "outputId": "6ba74eb6-0d60-419a-c39a-dd165cd49b60" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Height Score Course\n", + "2 1.695 NaN Unknown\n", + "5 1.778 NaN Physics\n", + "8 NaN 7.44 Physics\n", + "11 1.539 NaN Biology\n", + "15 NaN 5.44 Biology\n", + "24 NaN 8.08 Biology\n", + "29 2.020 6.83 Unknown\n", + "33 1.691 NaN Math\n", + "35 2.085 6.96 Unknown\n", + "38 1.376 6.54 Unknown" + ] + }, + "execution_count": 39, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "unknown_height_idx = pd.Index(np.random.choice(data_missing.index, 10, replace=False))\n", + "unknown_score_idx = pd.Index(np.random.choice(data_missing.index, 10, replace=False))\n", + "unknown_course_idx = pd.Index(np.random.choice(data_missing.index, 10, replace=False))\n", + "\n", + "data_missing.loc[unknown_height_idx, \"Height\"] = np.nan\n", + "data_missing.loc[unknown_score_idx, \"Score\"] = np.nan\n", + "data_missing.loc[unknown_course_idx, \"Course\"] = \"Unknown\"\n", + "\n", + "data_missing_idx = unknown_height_idx | unknown_score_idx | unknown_course_idx\n", + "\n", + "data_missing.loc[data_missing_idx].head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nmUJS9SzfC9Y" + }, + "source": [ + "Criamos o _pipeline_ com as seguintes etapas:\n", + "\n", + "1. Faça imputação dos dados, preenchendo os dados faltantes com a mediana dos dados presentes.\n", + "2. Faça a normalização dos dados no intervalo _default_ $[0, 1]$.\n", + "3. Crie novas variáveis através da expansão polinomial da variável original." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "9ypslSlEhGBr" + }, + "source": [ + "O `Pipeline` recebe uma lista de transformações representadas por tuplas de dois elementos. Cada tupla contém:\n", + "\n", + "* O nome para a etapa (ou transformação ou estimador). Isso vai ser útil para recuperar algumas informações do _pipeline_ mais a frente.\n", + "* Um objeto da classe do transformador ou estimador, já com seus parâmetros configurados." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "XqthBhA18ITd" + }, + "outputs": [], + "source": [ + "num_pipeline = Pipeline(steps=[\n", + " (\"imputer\", SimpleImputer(strategy=\"median\")),\n", + " (\"minmax_scaler\", MinMaxScaler()),\n", + " (\"poly_features\", PolynomialFeatures(degree=2, include_bias=False))\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3UVr1XWCfZID" + }, + "source": [ + "Depois da especificação do nosso _pipeline_, podemos aplicá-lo simultaneamente a diversas variáveis (desde que as transformações especificadas façam sentido).\n", + "\n", + "No exemplo abaixo, aplicamos esse _pipeline_ às variáveis `Height` e `Score` ao mesmo tempo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "Qh8kbymmDZqB", + "outputId": "0595019a-1288-4ea8-d18b-1d61dc44136b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.26553106, 0.48554913, 0.07050674, 0.12892838, 0.23575796],\n", + " [0.49098196, 0.44894027, 0.24106329, 0.22042158, 0.20154737],\n", + " [0.42184369, 0.48843931, 0.1779521 , 0.20604504, 0.23857296],\n", + " [0.55611222, 0.20231214, 0.30926081, 0.11250825, 0.0409302 ],\n", + " [0.36673347, 0.45086705, 0.13449344, 0.16534804, 0.2032811 ],\n", + " [0.50501002, 0.48843931, 0.25503512, 0.24666674, 0.23857296],\n", + " [0.40581162, 0.73410405, 0.16468307, 0.29790795, 0.53890875],\n", + " [0.33066132, 0.51445087, 0.10933691, 0.170109 , 0.26465969],\n", + " [0.41082164, 0.64547206, 0.16877442, 0.26517389, 0.41663418],\n", + " [0.33366733, 0.3699422 , 0.11133389, 0.12343763, 0.13685723]])" + ] + }, + "execution_count": 41, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline_transformation = num_pipeline.fit_transform(data_missing[[\"Height\", \"Score\"]])\n", + "\n", + "pipeline_transformation[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HoNf9vDJfrW8" + }, + "source": [ + "Para ficar mais claro a saída do _pipeline_, podemos utilizar os nomes das _features_ geradas através do método `get_feature_names()`. Para tornar ainda mais claro, substituímos o que é chamado `x0` por `Height` e `x1` por `Score`, que é inferido pela ordem das variáveis no _pipeline_." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "OJz5zvr2EeM3", + "outputId": "444fe35c-4e5e-4f9c-ef6a-152dd9bcd775" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Height_n', 'Score_n', 'Height_n^2', 'Height_n Score_n', 'Score_n^2']" + ] + }, + "execution_count": 42, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "poly_features = num_pipeline.get_params()[\"poly_features\"].get_feature_names()\n", + " \n", + "pipeline_columns = [old_name.replace(\"x0\", \"Height_n\").replace(\"x1\", \"Score_n\") for old_name in poly_features]\n", + "\n", + "pipeline_columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MBgEafF-gKA3" + }, + "source": [ + "Criamos um novo _data set_ com essas variáveis resultantes do _pipeline_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 237 + }, + "colab_type": "code", + "id": "q_xBepJGIAJm", + "outputId": "6126947b-ef3f-42db-84aa-4317ed5f79d3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Height_n Score_n Height_n^2 Height_n Score_n Score_n^2\n", + "0 0.265531 0.485549 0.070507 0.128928 0.235758\n", + "1 0.490982 0.448940 0.241063 0.220422 0.201547\n", + "2 0.421844 0.488439 0.177952 0.206045 0.238573\n", + "3 0.556112 0.202312 0.309261 0.112508 0.040930\n", + "4 0.366733 0.450867 0.134493 0.165348 0.203281\n", + "5 0.505010 0.488439 0.255035 0.246667 0.238573" + ] + }, + "execution_count": 43, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "height_score_normalized_poly = pd.DataFrame(pipeline_transformation, columns=pipeline_columns)\n", + "\n", + "height_score_normalized_poly.head(6)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "9imGtnaygRiX" + }, + "source": [ + "Podemos também criar outro _pipeline_ para a variável categórica `Course`. Como se trata de uma variável de natureza completamente diferente, precisamos especificar um _pipeline_ diferente com as seguintes transformações:\n", + "\n", + "1. Preencha os dados faltantes (`None`) com a classe `Unknown`.\n", + "2. Crie novas variáveis binárias com o `OneHotEncoder`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eZP_HTkchI5c" + }, + "source": [ + "Assim como no _pipeline_ anterior, especificamos cada etapa como uma tupla com um nome e um objeto de um transformador ou estimador:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "NMv_2lV7KxTM" + }, + "outputs": [], + "source": [ + "cat_pipeline = Pipeline([\n", + " (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=\"Unknown\")),\n", + " (\"one_hot_encoder\", OneHotEncoder(sparse=False, dtype=np.int))\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wK66jYTShV52" + }, + "source": [ + "Após a especificação do _pipeline_, podemos aplicá-lo à nossa variável `Course`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KIFWvPS7LNUA" + }, + "outputs": [], + "source": [ + "course_pipeline_transformation = cat_pipeline.fit_transform(data_missing[[\"Course\"]])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "quJ4ThBBhfBI" + }, + "source": [ + "Agora, utilizaremos o nome que demos à etapa do `OneHotEncoder` para recuperar esse transformador através do método `get_params()`. Depois de recuperado o `OneHotEncoder`, acessamos seu atributo `categories_` (primeiro índice `[0]`, pois poderíamos ter aplicado o _pipeline_ a mais de uma variável categórica):" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "Zurb-NVWM4sX", + "outputId": "1e7c2960-6ffb-4285-bb2d-691157302850" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Biology', 'Math', 'Physics', 'Unknown'], dtype=object)" + ] + }, + "execution_count": 46, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "course_columns = cat_pipeline.get_params()[\"one_hot_encoder\"].categories_[0]\n", + "\n", + "course_columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ABQDGjU_iDGS" + }, + "source": [ + "Utilizamos a saída do _pipeline_ e os nomes das categorias recuperados do transformador para criar um novo `DataFrame`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "colab_type": "code", + "id": "3ec56uIcMvll", + "outputId": "5707acac-8d67-4d74-eb02-d73b98f6340a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Math Physics Unknown\n", + "0 0.265531 0.485549 0.070507 ... 0 0 0\n", + "1 0.490982 0.448940 0.241063 ... 0 0 0\n", + "2 0.421844 0.488439 0.177952 ... 0 0 1\n", + "3 0.556112 0.202312 0.309261 ... 0 0 0\n", + "4 0.366733 0.450867 0.134493 ... 0 1 0\n", + "5 0.505010 0.488439 0.255035 ... 0 1 0\n", + "6 0.405812 0.734104 0.164683 ... 0 0 0\n", + "7 0.330661 0.514451 0.109337 ... 0 1 0\n", + "8 0.410822 0.645472 0.168774 ... 0 1 0\n", + "9 0.333667 0.369942 0.111334 ... 0 1 0\n", + "\n", + "[10 rows x 9 columns]" + ] + }, + "execution_count": 48, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_transformed = pd.concat([height_score_normalized_poly, course_discretized], axis=1)\n", + "\n", + "data_transformed.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1NLD-pyliXWO" + }, + "source": [ + "Vale ressaltar que:\n", + "\n", + "* Poderíamos utilizar também o `ColumnTransformer` para compor (por isso, ele se encontra no módulo `sklearn.compose`) múltiplos `Pipeline` em diferentes variáveis.\n", + "* Os `Pipeline` não servem apenas para a transformação dos dados de treinamento. Eles também podem (e devem) ser usados para submeter os dados de teste e até de produção aos mesmos procedimentos dos dados de treinamento." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "SbShR7kMZGwE" + }, + "source": [ + "## _Outliers_\n", + "\n", + "_Outliers_, os famosos \"pontos fora da curva\", são observações que não parecem seguir o mesmo padrão dos demais dados. Eles podem vir de distribuições diferentes, serem erros na coleta de dados, erros de medição etc.\n", + "\n", + "Eles influenciam nossas análises e os nossos algoritmos ao apresentar comportamento distoante do resto do _data set_, impactando na média, variância, funções de perda e custo etc. Se fizer sentido, eles devem ser removidos ou transformados antes de prosseguirmos com a análise.\n", + "\n", + "No entanto, devemos julgar com cautela sua remoção: __alguns _outliers_ são dados autênticos e devem ser estudados com atenção__. Por exemplo, a remoção de uma medição muito alta na temperatura de um reator seria um erro, pois essa medição pode estar nos indicando um potencial problema com o dispositivo.\n", + "\n", + "Abaixo estudamos algumas técnicas simples para encontrar _outliers_.\n", + "\n", + "![outlier](https://www.stats4stem.org/common/web/plugins/ckeditor/plugins/doksoft_uploader/userfiles/WithInfOutlier.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "u3bsTDv0pAN4" + }, + "source": [ + "Começamos criando uma cópia da variável `Height` do nosso _data set_ para não impactar o original:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "tQ7AQztcZkYx" + }, + "outputs": [], + "source": [ + "height_outlier = data.Height.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "VQNHBAu4pHcp" + }, + "source": [ + "Adicionamos dez _outliers_ que representam pessoas estranhamente baixas ou estranhamente altas para o padrão que estamos observando:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 208 + }, + "colab_type": "code", + "id": "nX2R3V0HZI0w", + "outputId": "6acbd63c-820e-485a-cde4-72a69fefe13d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "14 1.646795\n", + "18 1.696510\n", + "29 0.516665\n", + "38 2.943781\n", + "48 1.058498\n", + "49 1.326605\n", + "57 2.074231\n", + "66 1.831315\n", + "68 2.737088\n", + "96 1.966029\n", + "Name: Height, dtype: float64" + ] + }, + "execution_count": 50, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "height_outlier_idx = pd.Index(np.random.choice(height_outlier.index, 10, replace=False))\n", + "\n", + "too_short_idx = pd.Index(height_outlier_idx[:5])\n", + "too_tall_idx = pd.Index(height_outlier_idx[5:])\n", + "\n", + "height_outlier[too_short_idx] = np.random.normal(loc=1.30, scale=0.5, size=5)\n", + "height_outlier[too_tall_idx] = np.random.normal(loc=2.20, scale=0.5, size=5)\n", + "\n", + "outlier_idx = too_short_idx | too_tall_idx\n", + "\n", + "height_outlier[outlier_idx]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mwNbTzDnpoDL" + }, + "source": [ + "Note que nem todos dados gerados se tornaram realmente _outliers_. Como geramos de uma distribuição aleatória, corremos esse risco.\n", + "\n", + "No entanto, temos alguns dados estranhos como 0.51 m e 2.73 m." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "x5pwD_1EqRNZ" + }, + "source": [ + "No _boxplot_ padrão, os dados mais extremos são mostrados como pontos fora do alcance dos _whiskers_ (as barrinhas do _box plot_).\n", + "\n", + "No caso abaixo, notamos três pontos acima e três pontos abaixo do considerado \"dentro da faixa normal\"." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 695 + }, + "colab_type": "code", + "id": "hRMVhYz3b2KH", + "outputId": "9e090cef-804c-4f17-958b-5e25154662db" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(height_outlier, orient=\"vertical\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MOKP49JMqTog" + }, + "source": [ + "Uma primeira abordagem bem simples é encontrar os pontos do _box plot_ acima.\n", + "\n", + "Tudo que estiver fora da faixa $[Q1 - 1.5 \\times \\text{IQR}, Q3 + 1.5 \\times \\text{IQR}]$ é considerado um ponto anômalo para aquele padrão:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "z_h0zaVDce0N", + "outputId": "86b9e772-6438-4820-87ba-dab83a4b1dd8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Faixa considerada \"normal\": [1.18575, 2.24175]\n" + ] + } + ], + "source": [ + "q1 = height_outlier.quantile(0.25)\n", + "q3 = height_outlier.quantile(0.75)\n", + "iqr = q3 - q1\n", + "\n", + "non_outlier_interval_iqr = [q1 - 1.5 * iqr, q3 + 1.5 * iqr]\n", + "\n", + "print(f\"Faixa considerada \\\"normal\\\": {non_outlier_interval_iqr}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wsuVvr8hq4Rc" + }, + "source": [ + "Agora podemos identificar quais pontos encontram-se fora desse intervalo, ou seja, podem ser considerados _outliers_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "colab_type": "code", + "id": "hm78PWbhc9Dz", + "outputId": "ee3995ea-8a63-4c90-b3dd-57ba673887ee" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "29 0.516665\n", + "38 2.943781\n", + "48 1.058498\n", + "68 2.737088\n", + "91 2.272000\n", + "92 1.164000\n", + "Name: Height, dtype: float64" + ] + }, + "execution_count": 53, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "outliers_iqr = height_outlier[(height_outlier < non_outlier_interval_iqr[0]) | (height_outlier > non_outlier_interval_iqr[1])]\n", + "\n", + "outliers_iqr" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XcF70kmerGEq" + }, + "source": [ + "Se estivermos seguos de que esses pontos representam de fato _outliers_ e que sua remoção não traz prejuízo à nossa análise, então podemos removê-los:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "BVRJS9DNeb9z" + }, + "outputs": [], + "source": [ + "height_no_outlier_iqr = height_outlier.drop(index=outliers_iqr.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "urvTyUfHrVrJ" + }, + "source": [ + "Uma segunda abordagem é observar as estatísticas descritivas dos dados.\n", + "\n", + "Repare no histograma abaixo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 726 + }, + "colab_type": "code", + "id": "bc_paOePfHJ5", + "outputId": "6840da1c-bae6-4465-8aa7-87f69928e182" + }, + "outputs": [ + { + "data": { + "image/png": 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AXxCmAWDAmq225hYrlm7yON/kaFRzSxWZ7JsGgG0jTAPAgC0sV9Vqm7boTEtSOhVVo9nW\nUrFudSkA4HiEaQAYMDusxTvfxEhUkjS/xNw0AGwXYRoABswua/E6EtGgIiG/5hcJ0wCwXYRpABiw\nmWxZQ7Gg4pGg1aVIkgzDUDq1MjcNANgewjQADJhdNnmcLz0SVaHcUKXWtLoUAHA0wjQADNhMrmyb\nEY+OidTK4THMTQPA9hCmAWCAipWGCuWGdo3GrS7lAmNDEfkMaX6JkxABYDsI0wAwQDO51U0eNutM\n+/0+jQ5F6EwDwDYRpgFggDpr8TI2m5mWVlbkdXZgAwB6Q5gGgAGazpXk9xkaX51RtpN0Kqp221Qu\nz6gHAPSKMA0AAzSTLWtiJCq/z34/btMpDm8BgO2y3093AHARO67F64hFAkpEg5rj8BYA6BlhGgAG\npNVua26xosyYvTZ5nC+dWrkJ0TSZmwaAXhCmAWBAFpZWbu6za2daWjm8pVJrqVTh8BYA6AVhGgAG\nZNqma/HON7E6N83R4gDQG8I0AAxIZy2enTvTqWRYAb/BTYgA0CPCNAAMyEyupGQsqEQ0aHUpG/IZ\nhtKpKGEaAHpEmAaAAZnJ2neTx/nSqagW8zU1mm2rSwEAxyFMA8CA2Hkt3vnSqahMSQvLdKcBYKsI\n0wAwAOVqU/lywyFheuV0RvZNA8DWEaYBYAA6Xd7OKYN2Fgr6NZwIaWGZY8UBYKsI0wAwAPNLK8F0\nfLXra3fjQxFll6sc3gIAW0SYBoAB6HSmx4ft35mWpLHhiKr1lspVDm8BgK0gTAPAACwsVRUN+xWP\nBKwupSvjwysddEY9AGBrCNMAMADzyxWND0dlGIbVpXRlJBmWYUhZwjQAbAlhGgAGYGG5utbtdQK/\n36eRZFjZPGEaALaCMA0AfWaaphaWK47Y5HG+8WFuQgSArSJMA0Cf5csN1RttR3WmJWlsKKJ6s61C\nuWF1KQDgGIRpAOizhaXVTR4O60yPrYZ/5qYBoHuEaQDos/nOgS0O60ynEmH5fQYbPQBgCwjTANBn\nC50DWxyyY7rD5zM0OsRNiACwFYRpAOizheWKhmJBhUN+q0vZsrHhiHL5qtrchAgAXSFMA0CfzS9V\nHTcv3TE+HFGzZWq5WLe6FABwBMI0APTZwnLFcZs8OsaGuAkRALaCMA0AfdRum8rla47bMd0xFA8p\nGPBxEyIAdIkwDQB9lCtU1Wqbju1MG4ahsaEINyECQJcI0wDQR2ubPBzamZakseGwFvM1tdrchAgA\nmyFMA0AfOXXH9PnGhqNqm6YWCzWrSwEA2yNMA0AfLSxVZRjS6JBzw/Q4NyECQNcI0wDQRwvLFY0m\nwwr4nfvjNR4NKBz0E6YBoAvO/WkPADY0v1x13MmHFzMMQ2PDES2sjqwAADbWVZg+ceKEPvWpT+mq\nq67Sm2++ue5rHn30UX3iE5/Q0aNHdfToUT300ENrz1UqFX3lK1/RzTffrMOHD+vnP/95f6oHAJtZ\nWKpoPOXcEY+O8eGIlot1NVttq0sBAFsLdPOim266SXfeeafuuOOOy77u1ltv1QMPPHDJ49/97neV\nSCT0/PPP6/Tp07rjjjv03HPPKR6P91Y1ANhQo9nSUrGutMM709LKseKmpFy+qomRmNXlAIBtddWZ\nvvbaa5XJZHr+Ij/72c902223SZIOHDigj370o/rFL37R8/sBgB11DjpxQ2f6/ZMQ2egBAJfT15np\np59+WkeOHNHdd9+tl156ae3xc+fOac+ePWt/zmQympmZ6eeXBgDLrYVpF3Smo2G/IiE/6/EAYBNd\njXl04/bbb9e9996rYDCoF198Uffdd5+eeeYZjYyM9OX9x8YSfXkfL0qnk1aXgMvg+tjbVq5P9a0F\nSdKHrxzXWJeB2syVlUwMppMdDAa29d7jqaiWy/V13yMWCys9av34B98/9sb1sTeuT3/0LUyn0+m1\nj2+44QZlMhm99dZbuu6667R7925NTU1pdHRUkjQ9Pa3rr79+S++fzRbV5jSuLUunk5qfL1hdBjbA\n9bG3rV6fU+8tKeD3qVlraH6+2dXnlGtNFYqDWUHXaGzvvYdiQb1+pqTlfEU+n3HBc+VyTfOt1nZL\n3Ba+f+yN62NvXJ/1+XzGlhu4fRvzmJ2dXfv4tdde09TUlA4ePChJOnz4sH7wgx9Ikk6fPq2XX35Z\nN954Y7++NADYwsJSRWPDEfkMY/MXO8BIMqx221S+XLe6FACwra460w8//LCee+45LSws6K677lIq\nldLTTz+te+65R/fff7+uvvpqPfLII3rllVfk8/kUDAZ18uTJtW71F7/4RR07dkw333yzfD6fvvnN\nbyqRYGwDgLvML1UdfYz4xUaSYUnSYr6mVCJscTUAYE9dhenjx4/r+PHjlzz++OOPr3184sSJDT8/\nFovpW9/6Vg/lAYBzLCxXdHD3kNVl9M1wIiyfIeUKNR20uhgAsClOQASAPihXmypVm67qTPt9hoYT\nYTZ6AMBlEKYBoA86R2+Pp5y/Fu98I8mwFguDuUESANyAMA0AfTC/1Nkx7Z7OtLQSpiu1lqr17raT\nAIDXEKYBoA86nem0CzvTkhj1AIANEKYBoA8WlqqKhv2KR/q2vt8WRofe3+gBALgUYRoA+mB+uaLx\n4agMl+yY7oiEAoqG/crRmQaAdRGmAaAPsstV181Ld4wkI4x5AMAGCNMAsE2maWohX9XYkFvDdFjL\nxZpabdPqUgDAdgjTALBNlVpTtXpLY67tTIfVNqV8ie40AFyMMA0A25RdvTnPrZ3p0dWNHjluQgSA\nS7jrtnMAGIBmW6o1Nt6zPLVQkiRFowGValvbx+yEyYmheEg+n8HcNACsgzANAJuoNZr6j9dmN3z+\n9XcXJUlnZgqaX6xs6b3/64fS26ptJ/h8hlKJEGEaANbBmAcAbFOp2pTPMBQJ+a0uZWBWjhWvyTQd\n0EoHgB1EmAaAbSpVGopHA67bMX2+0WRE1XpLlVrL6lIAwFYI0wCwTaVqQ/FI0OoyBopjxQFgfYRp\nANimUrWpeNTdt6C8H6arFlcCAPZCmAaAbWi3TVWqTdd3psMhv2KRAJ1pALgIYRoAtqFcbcqUXN+Z\nlt6/CREA8D7CNABsQ6nakCTXd6allcNblkt1tVptq0sBANsgTAPANngpTI8kwzJNaalUt7oUALAN\nwjQAbEOpsnLioRfGPFKJlZsQl4uEaQDoIEwDwDaUqg2Fg34F/O7/cZqMh2QY0nKRuWkA6HD/T38A\nGKBipamEB7rSkuT3GUpGg1pmzAMA1hCmAWAbStWG4lH3z0t3DCfCjHkAwHkI0wDQI9M0V44S98DN\nhx3DiZDy5bqabPQAAEmEaQDoWb3ZVrNlKh7xxpiHJA3HQzJNaX6pYnUpAGALhGkA6FGpsroWz0Nj\nHp2NHrO5ssWVAIA9EKYBoEel6upaPA91pofiIUnSDGEaACQRpgGgZ17sTAcDPsUjAc1kCdMAIBGm\nAaBnpWpDPp+hSMhvdSk7ajgRZswDAFYRpgGgR6VKU/FIQIZhWF3KjhqOhzSbq6jdNq0uBQAsR5gG\ngB6Vqt5ai9cxnAip0WprIV+1uhQAsBxhGgB6VKo0FffI6YfnSyVWbkKcXihZXAkAWI8wDQA9aLdN\nlWtNb3am4yvr8aa5CREACNMA0ItyZy2eBzvT4ZBfyVhQ5+hMAwBhGgB6UayursXzYGdaknaNxjSd\nJUwDAGEaAHrQ2TGd8NCO6fNNjsZ0LluWabLRA4C3EaYBoAed0w9jHjr98Hy7xmKq1JpaKtatLgUA\nLEWYBoAelCoNRUJ+Bfze/DG6azQmSYx6APA8b/4WAIBtKlVXDmzxqvfDNBs9AHgbYRoAelCqNhT3\n6Ly0JA3FQ4qGA2z0AOB5hGkA2CLTNFWqePP0ww7DMLR7jI0eAECYBoAtqjfaarZMT495SFJmLK5z\njHkA8DjCNABsUamzY9rDYx6StHs8rnypruLqmkAA8CLCNABsUcnDpx+eLzPGRg8AIEwDwBZ1Dmzx\n8sy0JGXG45LY6AHA2wjTALBFpWpDPp+hSMhvdSmWGh+KKBTwsdEDgKcRpgFgi0qVlR3ThmFYXYql\nfD5Du0ZjOseYBwAPI0wDwBaVqt5ei3e+zHhc0wuMeQDwLsI0AGxRqdpUzONr8ToyYzFl81XVGi2r\nSwEASxCmAWAL2m1TFY8fJX6+yZGVjR7zixWLKwEAaxCmAWALKrWmTLHJo2NyNCpJml1k1AOANxGm\nAWALOjumYx7fMd0xkVrpTM/RmQbgUYRpANiCtdMP6UxLkmKRgJKxIJ1pAJ5FmAaALSh3Tj9kZnrN\n5EhMszk60wC8iTANAFtQqjYU9PsUDPDjs2NyJEpnGoBn8dsAALagVGkqFuXAlvNNjMa0VKyrVmc9\nHgDvIUwDwBaUqw1GPC4yObKy0WNuiVEPAN5DmAaALVg5sIWbD8/X2TU9m2PUA4D3EKYBoEutdlvV\neovO9EUmRtg1DcC7CNMA0KX3N3nQmT5fNBzQUDykWXZNA/AgwjQAdKlUWT2whc70JSZHoppjzAOA\nBxGmAaBLHNiyscmRmGa5ARGABxGmAaBLa2MeHCV+iYmRqJaLdVXrTatLAYAdRZgGgC6Vqg2Fg34F\n/PzovNjk6MpGjznmpgF4DL8RAKBLK2vx6EqvZ3JtowdhGoC3EKYBoEulCge2bKSzHm+O9XgAPIYw\nDQBdKlebike5+XA9kVBAw4mQZnN0pgF4C2EaALrQaLZVb7YZ87iMyVSUg1sAeE5XvxVOnDihZ599\nVlNTU3ryySf1oQ996JLXPPbYY3rmmWfk8/kUDAb11a9+VTfeeKMk6dixY/rXf/1XjYyMSJIOHz6s\nL3/5y338xwCAwWIt3uYmRmP69dtZq8sAgB3VVZi+6aabdOedd+qOO+7Y8DXXXHON7r77bkWjUb3+\n+uv6/Oc/r3/5l39RJBKRJP3xH/+xPv/5z/enagDYYe+ffkhneiOTI1HlS3VVak1Fw/x7AuANXY15\nXHvttcpkMpd9zY033qhodOUGlKuuukqmaWppaWn7FQKADXQ604x5bGxyhPV4ALxnIDPTP/nJT7R/\n/37t2rVr7bG///u/15EjR3Tffffp7bffHsSXBYCBef8occY8NtLZNc3cNAAv6XuL5d///d/1V3/1\nV/q7v/u7tce++tWvKp1Oy+fz6Sc/+Ym+9KUv6YUXXpDf7+/6fcfGEv0u1TPS6aTVJeAyuD72lk4n\nZebKarRMxSIBpYaifX3/YDCgZCLS1/fcifeOxcJKr4bnjuTqv5tivbVj/7/m+8feuD72xvXpj76G\n6Zdeekl/9md/pm9/+9u64oor1h6fnJxc+/jWW2/VX/zFX2hmZkZ79uzp+r2z2aLabbOf5XpCOp3U\n/HzB6jKwAa6PvXWuT7nW1FKhqlg4oEKx2tev0Wg0+/6eO/He5XJN863WJY+nEiGdOru0I/+/5vvH\n3rg+9sb1WZ/PZ2y5gdu3MY9f//rX+upXv6pvfetb+shHPnLBc7Ozs2sf//M//7N8Pt8FARsA7I7T\nD7szORLjFEQAntLVb4aHH35Yzz33nBYWFnTXXXcplUrp6aef1j333KP7779fV199tR566CFVq1V9\n/etfX/u8kydP6qqrrtIDDzygbDYrwzCUSCT013/91woE+KUEwBlM01S52tCe8bjVpdje5GhU/+ut\nBavLAIAd01WiPX78uI4fP37J448//vjaxz/60Y82/Px/+Id/2HplAGAT5VpTzZbJWrwuTI7ElC83\nWI8HwDM4AREANrFYqEmSYhwlvqmJETZ6APAWwjQAbGJpNUzTmd7c5MjKRo/ZHHPTALyBMA0Am1gk\nTHct3QnTdKYBeARhGgA2sVioyTCkCDPAmwoH/RpJhjkFEYBnEKYBYBNLhZpi4YB8hmF1KY4wORKl\nMw3AMwjTALCJxUJNcW4+7NrESFTzS4M5LAYA7IYwDQCbWCzUOLBlC8aHo8qX6qrVLz0hEQDchjAN\nAJfRNk0tFWuKR+hMdyudWrkJcWGZuWkA7keYBoDLKJTqarU5sGUrOmGaUQ8AXkCYBoDLyHUObCFM\ndy2dikiS5pfoTANwP8I0AFxGLr/SXWXMo3uJaFCRkJ8wDcATCNMAcBm5/OqBLVE6090yDEPjw1HC\nNABPIEwDwGXkClUF/T6Fg36rS3GUdCqihWVmpgG4H2EaAC4jl68plQzL4MCWLUmnVjrTpmlaXQoA\nDBRhGgAuI5evaiQZtroMx0nJt8vAAAAgAElEQVSnoqo328qX6laXAgADRZgGgMvIFVY609ga1uMB\n8ArCNABsoNVqa6lYozPdA9bjAfAKwjQAbCCbr8o0pZEEYXqrxocJ0wC8gTANABtYWA2CjHlsXTDg\n10gyrHmOFAfgcoRpANhAJ0wz5tGb9HCEmWkArkeYBoANEKa3p7MeDwDcjDANABuYX6ooEvIrGub0\nw16kU1EtFWpqNFtWlwIAA0OYBoANLCxVNDYUsboMxxpPRWRKnIQIwNUI0wCwgfmlikaGGPHoFbum\nAXgBYRoANrCwVNFoks50rzpheoGNHgBcjDANAOtoNFtaLtY1Sme6Z8PxkIIBHzchAnA1wjQArCNX\nqEkSneltMAxjdaMHYx4A3IswDQDryOVXwzSd6W0ZH47QmQbgaoRpAFhHLr/STR1lm8e2dHZNm6Zp\ndSkAMBCEaQBYR2fMgwNbtiediqpab6lYaVhdCgAMBGEaANaxmK8qGQspHPRbXYqjpVMrnX12TQNw\nK8I0AKwjV6gpPRK1ugzHe3/XNHPTANyJMA0A68jmq2tBEL0bH17pTBOmAbgVYRoA1pHL1zROmN62\nSCigoViQMA3AtQjTAHCRSq2pSq1JmO4Tdk0DcDPCNABcpLPJgzDdH531eADgRoRpALjI4uqOaWam\n+2M8FVUuX1Oz1ba6FADoO8I0AFyEznR/pVMRtU1z7d8rALgJYRoALpLLV2VIGhvm9MN+SA+zHg+A\nexGmAeAiuXxNQ4mQAn5+RPYDu6YBuBm/KQDgIrlCVWNDdKX7ZSQZlt9nEKYBuBJhGgAuks3XNJoM\nW12Ga/h8hsaGI1pgPR4AFyJMA8B5TNPUYr6qUTrTfZUejmhhmc40APchTAPAeUrVpurNNp3pPhsb\njmphmc40APchTAPAeXKrO6bpTPdXOhVRodxQtd60uhQA6KuA1QUAgJ3k8iu7kEeG6ExfjuEzVKp1\nH4yT8ZAk6ex8SbvH45d9bTgYUIBWDwCHIEwDwHlyhdXOdJLO9OXUGi396s35rl/f2eTxP1+Z0b6J\nxGVf+7uHJhUI8+sJgDPw3/4AcJ5cvia/z9DwaicV/ZGIBiVJxUrD4koAoL8I0wBwnlyhqlQiLJ/P\nsLoUV4mE/Ar4DRXLhGkA7kKYBoDz5PI1jTEv3XeGYSgeDdKZBuA6hGkAOE+OHdMDkyBMA3AhwjQA\nrGqbphYLNTZ5DAhhGoAbEaYBYFW+VFerbbLJY0AS0aAazbZqjZbVpQBA3xCmAWBVZ8f0KJ3pgVjb\n6MFNiABchDANAKs6px+OMTM9EIkY6/EAuA9hGgBWcZT4YLFrGoAbEaYBYFU2X1M46Fc8wul7gxAO\n+hUM+AjTAFyFMA0Aq1bW4oVlGBzYMihs9ADgNoRpAFiVzVeZlx4wwjQAtyFMA8AqDmwZvEQ0qFKl\nIdM0rS4FAPqCMA0AkuqNlvLlBkeJD1giFlSzZapaZ9c0AHcgTAOApMVCZ8c0nelBSrLRA4DLEKYB\nQCvz0hJhetDihGkALkOYBgC9H6YZ8xgsTkEE4DaEaQDQ+0eJjyTpTA9SMOBTJOSnMw3ANQjTAKCV\nTR7D8ZCCAX4sDhrr8QC4Cb81AECsxdtJccI0ABchTAOAVo4SZ156Z3R2TbfZNQ3ABQjTADzPNE06\n0zsoGQ2qbUqVatPqUgBg2zYN0ydOnNCnPvUpXXXVVXrzzTfXfU2r1dJDDz2kT3/607r55pv1xBNP\ndPUcANhBsdJQvdnmKPEdkoixHg+AewQ2e8FNN92kO++8U3fccceGr3nyySd15swZPffcc1paWtKt\nt96qT3ziE9q7d+9lnwMAO+hs8qAzvTMS5+2anrS4FgDYrk0709dee60ymcxlX/PMM8/oD//wD+Xz\n+TQ6OqpPf/rT+qd/+qdNnwMAO1jbMT3MzPROiEdX+jh0pgG4QV9mpqenp7V79+61P2cyGc3MzGz6\nHADYAacf7iy/z6dYOMDBLQBcYdMxD7sYG0tYXYJjpdNJq0vAZXB9rFdptBUK+HTF/lEZhnHBc+l0\nUmaurGRiMEE7GAx48r2HE2FV6q113yMWCys9Guvqffj+sTeuj71xffqjL2E6k8no3LlzuuaaayRd\n2I2+3HNbkc0W1W6zRmmr0umk5ucLVpeBDXB97OG92YJGkmEtLBQveLxzfcq1pgrF6kC+dqPhzfeO\nhv2azZXXfY9yuab5VmvT9+D7x964PvbG9Vmfz2dsuYHblzGPw4cP64knnlC73VYul9MLL7ygz3zm\nM5s+BwB2wFq8nZeIBlWuNmmSAHC8TcP0ww8/rN/7vd/TzMyM7rrrLv3BH/yBJOmee+7Ryy+/LEk6\nevSo9u7dq9///d/XH/3RH+lP/uRPtG/fvk2fAwA7yOarrMXbYYloUKakUpW5aQDOtumYx/Hjx3X8\n+PFLHn/88cfXPvb7/XrooYfW/fzLPQcAVmu22soX6xrl9MMddf56vGQsZHE1ANA7TkAE4GmLhZpM\nic70DlsL02z0AOBwhGkAnpbrrMUbJkzvpFgkIMNg1zQA5yNMA/C0tQNb6EzvKJ/PUDwSJEwDcDzC\nNABPy3aOEk8yM73TElHCNADnI0wD8LRcvqpkLKhQ0G91KZ5DmAbgBoRpAJ6WZce0ZRKxoCq1lpqt\nttWlAEDPCNMAPC2XrzEvbZHORo8S3WkADkaYBuBZpmmudKaZl7ZEIrpy1AGjHgCcjDANwLPKtaZq\n9RZjHhZJRFcOaykQpgE4GGEagGdll1fX4rFj2hLRsF8+n8GYBwBHI0wD8KxcYXUtHkeJW8IwjJWN\nHpyCCMDBCNMAPCvHgS2WS0QDzEwDcDTCNADPyuar8vsMDcVDVpfiWYlokJlpAI5GmAbgWbl8TaND\nYfkMw+pSPCsRDareaKvebFldCgD0hDANwLOy+SojHhZLxFb+VoCbEAE4FWEagGflOP3Qcp1d0wVu\nQgTgUIRpAJ7Uare1WKixycNi75+C2LS4EgDoDWEagCct5msyTTZ5WC0c9CvgN9joAcCxCNMAPCm7\nuhZvfDhqcSXe1tk1zUYPAE5FmAbgSQucfmgbKwe31K0uAwB6QpgG4ElrR4kzM225RCyoUqUp0zSt\nLgUAtowwDcCTFvJVDcdDCgb8VpfieYloUI1WW7VG2+pSAGDLCNMAPCm7XGXEwyY6Gz24CRGAExGm\nAXhSNl/VOGHaFgjTAJwsYHUBANAPzbZUa3S3q7htmsrlq7rmyjGVaht/jpkrq1xrqs0o70AlYoRp\nAM5FmAbgCrVGU//x2mxXry1Xm2q2TOXL9ct+TjIRUaFY1X/9ULpfZWIdoYBfoaBPRU5BBOBAjHkA\n8JzSage0M14A6yWiQTrTAByJMA3AczqhLREhTNtFIhpc+48cAHASwjQAzylWV0JbnM60bXQ60+ya\nBuA0hGkAnlOqNBQO+hUM8CPQLhKxoFptU5Vay+pSAGBL+E0CwHOKlabiUe6/thPW4wFwKsI0AM8p\nVRrcfGgzhGkATkWYBuAppmmqVG0ozs2HtkKYBuBUhGkAnlJrtNRsmXSmbSbg9yka9hOmATgOYRqA\npxQrKyceMjNtP/FIkINbADgOYRqAp3Bgi30lYhzcAsB5CNMAPKUTptkxbT/JaFClakPtNrumATgH\nYRqApxQrDQX9PoXYMW07iWhQpiktFWtWlwIAXeO3CQBPKVZXdkwbhmF1KbhIIrbytwULy1WLKwGA\n7hGmAXgKO6btKxkNSZIWlioWVwIA3SNMA/CUYqXBvLRNxaIB+Qw60wCchTANwDPqjZYazTadaZvy\nGYbi0SCdaQCOQpgG4BmlKps87C4ZC9GZBuAohGkAntE5sCUR4cAWu0rGglpYrsg0WY8HwBkI0wA8\no8iOadtLxoKq1FoqVZtWlwIAXSFMA/CMUqUhv89QJOS3uhRsIBlb2egxt8jcNABnIEwD8IzOWjx2\nTNtXcvVvDeaWyhZXAgDdIUwD8IxiZeXAFthX5+CWeTrTAByCMA3AM0rVhuIR5qXtLOD3aTgeYswD\ngGMQpgF4QrPVVrXeYse0A4ynIppj1zQAhyBMA/AENnk4x/hwlDANwDEI0wA8odTZMc3MtO2lU1Et\nF+uqNVpWlwIAmyJMA/CE0mpnmjEP+xtPRSRJ83SnATgAYRqAJxQrDfkMKRqmM21348NRSWz0AOAM\nhGkAnlCsNhSLsGPaCTqdaeamATgBYRqAJ3QObIH9xSNBxcIBwjQARyBMA/CEYqWxdiAI7C89EmXM\nA4AjEKYBuF6z1Valxo5pJ5lIsR4PgDMQpgG4XmfHdJIw7RgTI1Fll6tqtdtWlwIAl0WYBuB6xfLq\nWjzGPBwjnYqq1TaVy9esLgUALoswDcD1iuyYdpyJ1Mp6PEY9ANgdYRqA6xXKDQX8hiIhv9WloEsT\nI+yaBuAMhGkArldcXYvHjmnnSCXDCvh9dKYB2B5hGoDrFdkx7Tg+w1A6FaEzDcD2CNMAXM00TRXL\n7Jh2ojTr8QA4AGEagKvVGi01Wm0loyGrS8EWdXZNm6ZpdSkAsCHCNABXW9vkQWfacdIjUdXqLRVW\nVxsCgB0RpgG4WieIMTPtPKzHA+AEhGkArsaOaediPR4AJwh086JTp07p2LFjWlpaUiqV0okTJ3Tg\nwIELXvO1r31Nb7zxxtqf33jjDT322GO66aab9Oijj+r73/++JiYmJEkf//jH9eCDD/bvnwIANlAs\nNxQJ+RUM0DtwmvHhqAzRmQZgb12F6QcffFCf+9zndPToUf30pz/V17/+dX3ve9+74DUnT55c+/j1\n11/XF77wBd14441rj91666164IEH+lQ2AHSHtXjOFQz4NDIU1hydaQA2tmmrJpvN6tVXX9Utt9wi\nSbrlllv06quvKpfLbfg5P/zhD3XkyBGFQtw9D8BaBdbiOdpEKqp5OtMAbGzTMD09Pa3JyUn5/SvH\n8Pr9fk1MTGh6enrd19frdT355JP67Gc/e8HjTz/9tI4cOaK7775bL730Uh9KB4DLa7dNlaoNJelM\nOxa7pgHYXVdjHlvxwgsvaPfu3Tp06NDaY7fffrvuvfdeBYNBvfjii7rvvvv0zDPPaGRkpOv3HRtL\n9LtUz0ink1aXgMvg+vSHmSsrmYhc8Fi+VJdpSuMjsUue61YyEVEwGOj58zfDe18qFgsrPRqTJF2x\nb0T//OtpxZMRxSKX/kcR3z/2xvWxN65Pf2wapjOZjGZnZ9VqteT3+9VqtTQ3N6dMJrPu63/0ox9d\n0pVOp9NrH99www3KZDJ66623dN1113VdaDZbVLvN4v6tSqeTmp8vWF0GNsD16Z9yralCsXrBY7PZ\nsiQp4NMlz3UjmYioUKyq0bj0vfuF975UuVzTfKslSUqGV/5W9DdvzulgZuiC1/H9Y29cH3vj+qzP\n5zO23MDddMxjbGxMhw4d0lNPPSVJeuqpp3To0CGNjo5e8tqZmRn98pe/1JEjRy54fHZ2du3j1157\nTVNTUzp48OCWCgWArSpU6pLE6YcOtmu1Qz2z+h9GAGA3XY15fOMb39CxY8f07W9/W0NDQzpx4oQk\n6Z577tH999+vq6++WpL04x//WJ/85Cc1PDx8wec/8sgjeuWVV+Tz+RQMBnXy5MkLutUAMAjFckOG\nIcUifZ9oww6ZGInKMKTpHGEagD119Rvmyiuv1BNPPHHJ448//vgFf/7yl7+87ud3wjcA7KRCpaF4\nJCifz7C6FPQo4PcpPRzVDGEagE1xigEA1yqyFs8Vdo3FGPMAYFuEaQCuVaywFs8Ndo3GNLdYVtvk\nJnQA9kOYBuBKjWZb1XqL0w9dYNdoTPVmW7n8YLaHAMB2EKYBuFKx0pAkxjxcYG2jB3PTAGyIMA3A\nlTphmjEP59s1xno8APZFmAbgSsUynWm3GI6HFAn56UwDsCXCNABXKlTqCvgNhYN+q0vBNhmGoV2j\nMc0SpgHYEGEagCsVyw0lYyEZBjum3WDXWIzONABbIkwDcKVipcEmDxfZNRpTNl9TrdGyuhQAuABh\nGoDrmKZJmHaZzkYPRj0A2A1hGoDrVOstNVsmNx+6COvxANgVYRqA67AWz30mCdMAbIowDcB1CqzF\nc51w0K+xoTBhGoDtEKYBuM7a6Yd0pl1l12iMg1sA2A5hGoDrFMp1RcN+Bfz8iHOTXaNxzeTKMk3T\n6lIAYA2/aQC4TmF1xzTcZddYTNV6S8ulutWlAMAawjQA1ymU6xoiTLvO2kYPRj0A2AhhGoCrNJpt\nVWotJePMS7sN6/EA2BFhGoCr5MsrIwB0pt1nZCisUMBHmAZgK4RpAK7SWYuXZC2e6/gMQ5OjMcI0\nAFshTANwlcLqzWncgOhOk6zHA2AzhGkArpJfXYsXDPDjzY12jcY0v1xRo9m2uhQAkESYBuAyhXKD\neWkXy4zGZJrS3FLF6lIAQBJhGoDL5Et1JeOEabfaNcZ6PAD2QpgG4Br1ZkvVeoubD13s/fV4JYsr\nAYAVhGkArlEorWzyYMzDvaLhgIbjITZ6ALANwjQA1yh0dkxzYIurZcZimmbMA4BNEKYBuEZ+dcd0\nIkpn2s32jCc0tVBSu21aXQoAEKYBuEehVFc0HGAtnsvtmYirVm9pbpHuNADr8RsHgGvky3UNcfOh\n6+1NJyRJ707nLa4EAAjTAFykUG6wFs8D9ozHJUmnZwjTAKxHmAbgCpVaU9V6i860B0TDAY0PR/Tu\ndMHqUgCAMA3AHeZXT8RLshbPE/amEzrNmAcAGyBMA3CFTpgeYszDE/ak45qaL6rRbFtdCgCPI0wD\ncIX3O9OMeXjB3nRC7bap6SwnIQKwFmEagCvML1YUCwcU8PNjzQv2plduQpyaJ0wDsBa/dQC4wvxS\nVUlOPvSMydGYAn5D780XrS4FgMcRpgG4wvxSRUPcfOgZAb9PeyeSeo/ONACLEaYBOF652lSx0mBe\n2mMOZIboTAOwHGEagOPNrh4rzSYPb/lAZkiLhZpK1YbVpQDwMMI0AMfrhGl2THvLgcyQJG5CBGAt\nwjQAx5tbZC2eF31g10qYZtQDgJUI0wAcbzZXUSoRYi2ex4ynIoqGA9yECMBS/OYB4Hhzi2WlR6JW\nl4EdZhiG9qTjdKYBWIowDcDxZhcrmkgRpr1obzqhqfmSTNO0uhQAHkWYBuBopWpDxUpD44RpT9qb\njqtSayqXr1ldCgCPIkwDcLTOzYdpwrQn7U0nJHETIgDrEKYBONpsbmUtHmHam/ak45II0wCsQ5gG\n4GjT2bIMgzDtVfFIUCPJMLumAViGMA3A0c5lS5pIRRUM8OPMq/amE3SmAViG3z4AHG06W1ZmLG51\nGbDQ3nRc09mymq221aUA8CDCNADHarbams2VlRmPWV0KLLQ3nVCrbWpmdX4eAHYSYRqAY80vVdRq\nm9pNZ9rTuAkRgJUI0wAc69zCSidy9zhh2ssyY3H5fYbem+MmRAA7jzANwLGmsyvhadcoYx5eFgz4\ntHs8rndn8laXAsCDCNMAHOtctqTRobCi4YDVpcBiBzNJnZ4pcKw4gB1HmAbgWNMLbPLAigOZIZWq\nTc0tVawuBYDHEKYBOFLbNDWdK3HzISRJB3cNSZJOTTPqAWBnEaYBOFJuuap6o81aPEha2egRDPh0\nerpgdSkAPIYwDcCRzmVXN3nQmYakgN+n/ZMJOtMAdhxhGoAjdTZ5sBYPHQd3Dend2YJabU5CBLBz\nCNMAHOncQknJWFCJaNDqUmATBzNDqjfaml7gJEQAO4cwDcCRprNs8sCFDmSSkrgJEcDOIkwDcBzT\nNDWdLTHigQtMjsYUDft1aoabEAHsHMI0AMfJl+oqVZvKjLHJA+/zGYYO7BqiMw1gRxGmATjO2iYP\nOtO4yIFMUu/NFdVochMigJ1BmAbgOGubPJiZxkUO7hpSq23q7FzR6lIAeARhGoDjnFsoKRLyK5UI\nWV0KbOZghpMQAewswjQAx5nOlrV7PC7DMKwuBTYzOhTWUCyo04RpADukqzB96tQp3XbbbfrMZz6j\n2267TadPn77kNY8++qg+8YlP6OjRozp69KgeeuihtecqlYq+8pWv6Oabb9bhw4f185//vG//AAC8\n51y2xM2HWJdhGDqQGWKjB4AdE+jmRQ8++KA+97nP6ejRo/rpT3+qr3/96/re9753yetuvfVWPfDA\nA5c8/t3vfleJRELPP/+8Tp8+rTvuuEPPPfec4nHmHQFsTbna0HKxzrw0NnQwM6SX386qUmsqGu7q\n1xwA9GzTznQ2m9Wrr76qW265RZJ0yy236NVXX1Uul+v6i/zsZz/TbbfdJkk6cOCAPvrRj+oXv/hF\njyUD8LLOJo8MmzywgYOZpExJZ2bpTgMYvE3D9PT0tCYnJ+X3+yVJfr9fExMTmp6evuS1Tz/9tI4c\nOaK7775bL7300trj586d0549e9b+nMlkNDMz04/6AXjM9EJnkwdjHljfgbWbEAnTAAavb3//dfvt\nt+vee+9VMBjUiy++qPvuu0/PPPOMRkZG+vL+Y2OJvryPF6XTSatLwGVwfbZmsdxQMODTh//LhPy+\n929ANHNlJRORvn+9ZCKiYDAwkPeWxHuvIxYLKz3a3X8srff9k5Y0MRLV9GKF7y+L8e/f3rg+/bFp\nmM5kMpqdnVWr1ZLf71er1dLc3JwymcwFr0un02sf33DDDcpkMnrrrbd03XXXaffu3ZqamtLo6Kik\nlW739ddfv6VCs9mi2m1zS5+DlW+U+Xm6M3bF9dm6d95b0q7RmHLZC/cIl2tNFYrVvn6tZCKiQrGq\nRqP/793Be1+qXK5pvtXa9HWX+/7ZP5HQ66ezfH9ZiJ9v9sb1WZ/PZ2y5gbvpmMfY2JgOHTqkp556\nSpL01FNP6dChQ2vBuGN2dnbt49dee01TU1M6ePCgJOnw4cP6wQ9+IEk6ffq0Xn75Zd14441bKhQA\npJUd02zywGYOZoY0v1RVsdKwuhQALtfVmMc3vvENHTt2TN/+9rc1NDSkEydOSJLuuece3X///br6\n6qv1yCOP6JVXXpHP51MwGNTJkyfXutVf/OIXdezYMd18883y+Xz65je/qUSCsQ0AW1NrtJRdrur/\nvCaz+YvhaQfOO7zl6ivGLK4GgJt1FaavvPJKPfHEE5c8/vjjj6993AnY64nFYvrWt77VQ3kA8L7p\nbEmmOEYcmzuYScpnGHrz7BJhGsBAcQIiAMc4O7syJ71vkr/ZwuVFQgEdyCT15tklq0sB4HKEaQCO\ncWauqHDIr3QqanUpcICr9qV0ajqvemPzmxkBoFeEaQCOcXa2oH3phHyGsfmL4Xkf2pdSs2Xq7XN5\nq0sB4GKEaQCOYJqmzs4XGfFA1z64NyXDkN44s2h1KQBcjDANwBEWlquq1FraP0GYRndikYD2TzA3\nDWCwCNMAHOHM6s2H+yc5sQvdu2p/Sm+fy6vRbFtdCgCXIkwDcISzcwUZhrRnnLV46N5V+1JqNNs6\nNc3cNIDBIEwDcIQzs0XtGo0pFPRbXQoc5IP7UjLE3DSAwSFMA3CEs3MFRjywZYloUHvSCb3B3DSA\nAenqBEQAsFKp2lA2X9OnuPnQEwyfoVKtuenrzFxZ5S5ed+WeIf2P38woX64rFgkpQBsJQB8RpgHY\nHicfekut0dKv3pzf9HXJRESFYrWr96w323r2387o//5vBxQI86sPQP/w3+cAbO/M3GqYnmDMA1s3\nObpyYubMYtniSgC4EWEagO2dnS1oOB7ScDxkdSlwoEgooOF4SLO5itWlAHAhwjQA2zszx8mH2J7J\n0ajmFytqtU2rSwHgMoRpALbWbLV1bqGk/Yx4YBsmR2NqtNqaWh0ZAoB+IUwDsLVzCyW12qb205nG\nNkyOxCRJb00tW1wJALchTAOwtbNrNx8SptG7WCSgZCyo377HvmkA/UWYBmBrZ2aLCgV9a51FoFeT\nozG9PZVXm7lpAH1EmAZga2fnCtqbTsjnM6wuBQ63azSmSq2pd2cLVpcCwEUI0wBsyzRNnZktaj8j\nHuiD3eMxGZJefjtrdSkAXIQwDcC2svmqyrWm9k2yyQPbFwkF9IFdSf36HcI0gP4hTAOwrc7Nh3Sm\n0S8fOTiqU+fyypfrVpcCwCUI0wBs6+xsUYakvWnCNPrjdw6OypT0G7rTAPqEMA3Ats7MFTUxGlM4\n5Le6FLjE3omEhuIh/Zq5aQB9QpgGYFtnZguMeKCvfIaha64Y02/eyanVbltdDgAXIEwDsKVCua6F\n5ao+sIubD9Ff11w5pnKtqben8laXAsAFCNMAbOmdcytB58rdQxZXArf5nQOj8vsMvczcNIA+IEwD\nsKW3zy3LZxg6sIswjf6KRQL64N5h/eq3hGkA20eYBmBLb0/ltW8iwc2HGIirrxzTe/NF5fJVq0sB\n4HCEaQC2026bemc6ryv30JXGYFxzxZgkMeoBYNsI0wBsZ2qhpFq9pSt3D1tdClxq93hcY0MRVuQB\n2DbCNADbefvcsiTRmcbAGIaha64c06unF9VosiIPQO8I0wBs5+2pZSWiQaVTUatLgYtdfeWYao2W\n3nxvyepSADgYYRqA7bxzLq//smdYhmFYXQpc7NAHRhTw+/RrtnoA2AbCNABbKVYams6WdQX7pTFg\n4aBfH/5ASr96e0GmaVpdDgCHIkwDsJW1w1r2cPMhBu/jH0xrbrGis3NFq0sB4FCEaQC28s65ZRmG\ndDDDMeIYvP/9qrR8hqF/f23O6lIAOFTA6gIA4HxvTy1rbzqhSIgfT+g/w2eoVGuu/dnn9+mq/Sn9\n26uzOvx/7N/WnH44GFCAFhXgOfy2AmAbbXPlsJbrf2eX1aXApWqNln715vwFj40kw3rt3UX90/98\nV+Pb2CDzu4cmFQjzaxXwGv4bGoBtTC+UVKm1/v/27jy8rfLOF/j3HC2WZcm2bGvzviR2nHjJSlYT\nkgJJh9CkTANtgU6fXsIDDM2dtIWklxamQO9t2nk6bZkULrRl67SluYUUkkBCCBAnIZB98ZbE+yLL\ni+RV1n7uHw4G1wlxHIUHxxMAACAASURBVNtHsr6ff2xZR6+/1vHR+enV+74HOZx8SJMo3ayDKAio\na+2VOwoRhSEW00QUMqo5+ZBkoFYpkGyMQZ2tl6t6ENE1YzFNRCGjurkbMRolzAZerIUmV6ZFD5fH\njzbngNxRiCjMsJgmopBR3dKDHF6shWSQZtJBIXKoBxFdOxbTRBQSXG4fWjr6OV6aZKFSikg16VDf\n2otgkEM9iGj0WEwTUUiosQ2Ol87meGmSSaZFD7c3ALvTJXcUIgojLKaJKCTUNPdAAJBtZc80ySPF\nGAOlQkCtjUM9iGj0WEwTUUi42NyNZGMMorlOL8lEqRCRZtKhwd6LAId6ENEosZgmItn5A0Gcb+pC\nXlq83FEowmVZY+H1BWHr7Jc7ChGFCRbTRCS76uZueH1BzMxMkDsKRThrUgzUShG1l9Y8JyK6GhbT\nRCS78jonBAGYkc6eaZKXQhSQaY1Fg70PHm9A7jhEFAZYTBOR7MrrHciyxkKrUckdhQi5aXEIBCVU\nt3TLHYWIwgCLaSKSlcvtR21LL2ZmGuSOQgQASIjVwBivwfnGbl5enIiuisU0EcmqqtGJoCRhZgbH\nS1PoyE2LR0+/F3YHLy9ORF+MxTQRyaqizgm1UkQOL9ZCISTDoodaJaKqsUvuKEQU4lhME5Gsyuud\nyE2Lh0rJlyMKHUqFiJzkODTYezHg8csdh4hCGM9eRCQbZ68HLR39XBKPQlJuWjwkCbjYxImIRHRl\nLKaJSDYV9Q4A4ORDCklxOjUsCVpcaOpGkBMRiegKWEwTkWzK65zQRauQatLJHYXosnLT4tA34IOt\ng1dEJKLLYzFNRLKQJAnldQ7MzDRAFAS54xBdVppZD41agapGDvUgostjMU1EsrB1utDV5+V4aQpp\nClHAtNQ4NLf1oX/AJ3ccIgpBLKaJSBbldZfGS2dwvDSFtumpcZAAVDVwmTwiGonFNBHJorzOCVN8\nNJLio+WOQvSF9Fo1Mix6VDV0weMNyB2HiEIMi2kimnSBYBCVDU6u4kFhoygnEb5AEBX1TrmjEFGI\nYTFNRJOu1tYLtzfA8dIUNgz6KKSbdaiod8LrY+80EX2GxTQRTbqyWgcEADM4XprCSFFOInz+ICrZ\nO01En8Nimogm3cnz7chJiYMuWiV3FKJRS4jVINWkQ3m9E14/e6eJaJBS7gBEFDn8QaC5vRcNbX34\n6o3Z6Pf4x63tIC9QR5OgKCcRuz/qQ1V9FwpzEofdJ4jCuP5Pf16USgklu7+IQhKLaSKaNB6fH28d\nrgMACACOVtjHre3iXOO4tUV0JUlxGqQYY1Be58SMDANUn6twPb4ATp9vn5DfuyDfDGUUT9lEoYjv\nc4loUjW09iIxNgo6LYd4UHgqykmExxdAVSPXnSaiUfZM19bWYsuWLejq6kJ8fDy2bt2KzMzMYdts\n27YNu3fvhiiKUKlU2LRpE0pKSgAAW7ZsweHDh2EwDE42Wr16NR588MHx/UuIKOQ5ez3o6HZjzvQk\nuaMQjZkxPhrWRC3Kax2YkR4PpYL9UkSRbFTF9BNPPIFvfvObWLt2Lf7+97/j8ccfxyuvvDJsm6Ki\nInznO99BdHQ0Kisrcc899+DgwYPQaDQAgPvvvx/33HPP+P8FRBQ2Tl/sAABkWPQyJyG6PsXTEvHO\nx404V+PAbL45JIpoV3073dnZifLycqxZswYAsGbNGpSXl8PhcAzbrqSkBNHRg1cyy8vLgyRJ6Ori\nR2BE9JnTFzoQr1MjNkYtdxSi62IyaJFp0eNcrQO9Lq/ccYhIRlftmbbZbDCbzVAoFAAAhUIBk8kE\nm82GhITLX3Bhx44dSE9Ph8ViGfrZiy++iNdeew1paWn4/ve/j5ycnGsKmpiou6bt6TNGI3sBQ1mk\n7B9nrxvVzd2Yn2+GXqcZ9/ZVKuWEtKvXaSasbWDickdK29eaYTxz3zQvDf+9pxLHz3dgzdKsCX1O\ntNooGBO0E9L2RIqU17dwxf0zPsZ9avAnn3yCX//61/jDH/4w9LNNmzbBaDRCFEXs2LED9913H/bt\n2zdUoI9GZ2cfglz76poZjXq0t/fKHYOuIJL2zwcnmyEBsCREo7fPPe7t+3z+cW9Xr9Ogt889IW1/\nim2Pve1P989EtD1axdMScayyHeU1HchJiZ2w58Tl8qA9EF5rW0fS61s44v65PFEUrrkD96rDPKxW\nK+x2OwKXDuJAIIC2tjZYrdYR2548eRKPPPIItm3bhuzs7KGfm81miOLgr1q3bh1cLhdaW1uvKSgR\nhbfjVW0wxmsQr+MQD5o6ZqQbEK9T45OKNnh4mXGiiHTVYjoxMRH5+fnYuXMnAGDnzp3Iz88fMcTj\nzJkz2LRpE37zm99g1qxZw+6z2z9bS7a0tBSiKMJsNo9HfiIKA30DPlQ2dKF4mhGCIMgdh2jciKKA\nhbPMcLn92PdJo9xxiEgGoxrm8e///u/YsmULfvvb3yI2NhZbt24FAGzYsAEbN25EYWEhfvKTn8Dt\nduPxxx8fetzPf/5z5OXlYfPmzejs7IQgCNDpdHj22WehVHLxeaJIcepCBwJBCbOnJ6HN6ZI7DtG4\nMhu0yEmJxQcnm7FmSQbidVFyRyKiSTSqijYnJwfbt28f8fMXXnhh6Pu//e1vV3z8Sy+9dO3JiGjK\nOF7VhsTYKKSbdSymaUqam2tES0c/Pilvwy0LUvkJDFEE4UrzRDShBjx+lNU5MDfXxAKDpqzoKCVu\nW5KJVocLlQ1cFpYokrCYJqIJdepCB/wBCfPyjHJHIZpQiwosSDXG4HhlOzp7JmZVDyIKPSymiWhC\nfXiqGSZDNKalxskdhWhCCYKAJYUWRKkVKD3VAp8/KHckIpoELKaJaMI0d/TjfFM3lhcnQ+QQD4oA\nGrUSJUVW9Lh8+KTCfvUHEFHYYzFNRBPmw1PNUIgClhaOXJeeaKqyJGpRlJOI6uYe1LT0yB2HiCYY\ni2kimhBeXwAfnWvFvDwjYmN4oRaKLEU5iTAZonGkrBU9/V654xDRBGIxTUQT4lhVG/rdfiyfnSJ3\nFKJJJ4oCSoqsEEUBB063wB/g+GmiqYrFNBFNiA9OtcBsiMaM9Hi5oxDJIiZahaWFVjh6PDhSZock\nSXJHIqIJwGKaiMZdc3sfLjZ1Y/nsFK4tTREtzaRD8bRE1LT0cP1poimKxTQRjbsPT7VAqRCwtNAi\ndxQi2RXlJCLVpMOxyja0OngFUKKphsU0EY0rry+Aw+daMS/PBL2WEw+JBEHAsiIL9Fo1DpxqQd+A\nT+5IRDSOWEwT0bg6WtkGl8ePm2Ynyx2FKGSolQqsmJOMQEDChyebOSGRaAphMU1E4+qDU82wJGiR\nm8aJh0SfF6eLwrJiKzp7PPiYExKJpgwW00Q0bmptPahu7sHy2cmceEh0GWkm3eAFXTghkWjKYDFN\nRONmR2ktYjRK3FjMIR5EV1I8LRGpxhhOSCSaIlhME9G4uNjcjbM1nfjyogxERynljkMUsgYnJFqH\nJiT2c0IiUVhjMU1E4+KNAzXQa1X40txUuaMQhTy16rMJiR+c5BUSicIZi2kium5VDU5U1DvxT4sy\nEKVWyB2HKCzE6aKwtMiCzh43JyQShTEW00R0XSRJwhultYjTqbFiTorccYjCSrpZPzQh8Xxjt9xx\niGgMWEwT0XUpr3fifGMX1izOhFrFXmmia1U8LRHJSVocq2xDV59H7jhEdI1YTBPRmEmShB2lNTDo\no3BjsVXuOERhSRAELCmwQqkQUXrahkCQ46eJwgmLaSIas7M1DlQ39+D2JZlQKdkrTTRWWo0SSwot\ncPZ6cPJ8h9xxiOgasJgmojEJBiW8caAGSXEaLCtirzTR9Uoz6ZCbFofyOidaOvrljkNEo8RimojG\nZM/RBtTbe3HH8mwoFXwpIRoP82eYEBujxqGzrXB7A3LHIaJR4BmQiK5Zc0c/3jhQi7m5RizMN8sd\nh2jKUCpElBRZ4fH6caSslcvlEYUBFtNEdE0CwSD+sKscGrUC967KgyAIckcimlIS4zSYnWtEg70P\ntbYeueMQ0VWwmCaia/L2kQbU2npx76o8xMWo5Y5DNCXNzDQgKU6DoxXtcHv9cschoi/AYpqIRq2p\nrQ9/P1iLBTNMWDDDJHccoilLFAQsLrDA5w/gWGW73HGI6AuwmCaiUfEHgvjdrnLEaJS459ZcueMQ\nTXkGfRRmZSeipqUHFfUOueMQ0RWwmCaiUXnzUC0a7H24d9UM6LUc3kE0GYqyExAbo8Zf9l2Ah6t7\nEIUkFtNEdFUfnmrGzsP1WFpowbw8o9xxiCKGQiFi8SwzHD0e7DhYI3ccIroMFtNE9IWOVbbhlT1V\nKMxOxL+sniF3HKKIY07QYmmhBXuPNqKulat7EIUaFtNEdEVldQ48/1YZcpLj8NBXC3hxFiKZfGVZ\nNmJj1Hjp7UoEgkG54xDR5/DMSESXVdPSg//621lYErT4n+uLEKVSyB2JKGJpNUrcfXMuGux92H+8\nWe44RPQ5LKaJaISmtj78avtp6LUqfO+u2YjRqOSORBTx5uUZUZidiDdKa+Ds9cgdh4guYTFNRMMc\nKWvF068eg0Ih4Adfn414XZTckYgIgCAIuPvWXASCEl7bf0HuOER0CYtpIgIA+PxBvLqnCs+/VY5M\nsx6P/8sCmAxauWMR0eeY4qNx2+IMfFLRhrJarj1NFApYTBMROroG8H/+eBzvn2zG6oXpeOSbc2DQ\ns0eaKBR9eWEGzIZovLq3Cj4/154mkhuLaaIIFggG8cGpZvzkpaOwOwfw8B2FuHPFNChEvjQQhSqV\nUsQ9t+ahzTmAtz9ukDsOUcRTyh2AiCafJEk4U92Jv75/EbZOF6anxuF/3JbPYR1EYWJWVgJuyDdh\n5+F6LJpp5rFLJCMW00QRpr61F399/yIq6p0wG6Lx8B2FmDM9CYIgyB2NiK7BXSun40x1J/747nls\nWl/MY5hIJiymiSJAUJJwrqYT7x5tRFmdE7poFe6+JRfLZyfzQixEYcqgj8JXS7Lx5/cu4HhVO+bP\nMMkdiSgisZgmmsI83gAOn7Ph3WNNaHW4EK9T444bs7Fybiq0Gh7+ROFu5bwUHDprw5/fu4BZWQmI\njuJxTTTZeNQRTUGOHjfeO96ED0+1wOXxI8uqx/1fmYn5eSb2RBNNIQpRxL2r8vDTV4/jzUO1uGvl\ndLkjEUUcFtMRxB8EPD7/hLQdpVJCyRptmNE835LDBZfn2veJSqmEzz/ycbW2HnxwohmnLrRDAjB7\nWhJumpuCLGssBEGAxx+Exx8cU9vjIShNSLNEES0nJQ43Fifj3aNNWFpgRapJJ3eksDeR58uJfI3l\nuVgeLKYjiMfnx9EK+4S0vSDfDCU/XhxmNM+3XqdBb5/7mtsuzjXi9Pl2AIMrc7R0uHCmuhPtXQNQ\nKUXMyDBgRoYBumgVOrvd6Owe/e/4fNvjrTjXOCHtEkW6r92UgxPn2/HK3ipsuXsuRE5GvC4Teb6c\nyNdYnovlwWecKExJkoSm9n6cqe5EZ7cbWo0SC/JNmJYSBxW7Jogiii5ahfUrcvDi7kocOmtDSVGy\n3JGIIgaLaaIwVFHnwM7D9XD2eqCLVmHxLDOyU+KgENkbRRSplhZaUXrGhu3vV2POdCN00Sq5IxFF\nBHZfEYWRnn4v3jvehN+9WQ5/IIilhRasK8nC9LR4FtJEEU4UBHzr1jy43H78df9FueMQRQz2TBOF\nAa8/gDMXO1FZ74RCFHH7skzEatUsoIlomFSTDl9elI5dH9VjzvQkzOE8BaIJx55pohBX39qLHQdq\nUV7nRHZyHNbdmIWb5qaykCaiy1q7LAvpZh1eeqcSPf1eueMQTXkspolClNvrx4FTLfjwVAtiNEr8\n0+IMLCm08KIMRPSFlAoRG9bMxIAngJferoQkcU1KoonEYpooBDXYe/HmwTo02HsxZ3oSvrwoA0lx\nGrljEVGYSDHq8LXl2Th1sQOlZ2xyxyGa0tjFRRRCvL4APi63o9bWi4TYKNyyIA0GfZTcsYgoDN28\nIA2nLnbgz+9dwIwMA0zx0XJHIpqS2DNNFCLanC68dagOda29KJ6WiH9alMFCmojGTBQE/I/bZkIU\ngN/vLEeQlyAlmhAspolkFgxKOH2xA3s+boQgCPjywnQUT0uCyAmGRHSdEuM0uPuWXFxo6saOg7Vy\nxwk7kiTBHwhiwONHT78X3X1eeHwBWcehB4JBeLwBDHj86Bvwoaffi55+L/yBoGyZIh2HeUxhLrcf\nrZ39aGrtxYDHD0efBw32XsTFREGvVbFYCwG9Li/2Hm1Em3MA2cmxuGGmCWqlQu5YRDSFLJ5lQWVD\nF3YeroM1QYvFBRa5I4Ukjy+AmuZuXGjqRkWDE3W2Xnj9AVyubhYFAZooBaLVCsREqxCrVSM2Ro04\n3eDXKNX1vY77A0H0ugYL5V6Xd/D7S19dbv8VH/f2kQZYErQwJ2iRkhSD2dOSkMj5NhOOxfQU4vMH\ncL6pG+dqOnGuxoHmjv4rbiuKAuJi1DDoo2CMj0amRY8oNYu4yVTf2osjZXYEgxKWFVmQnRwndyQi\nmoIEQcC3VuWho2sAL75dgcQ4DXLT4uWOFRIGPH4cKbfj8Dkb6my9CAQlCACSjTFIN+sQpVZApRSh\nVopQXerocHv9GPAE4Pb64fYE0N3nRVNbHz4/ikatEqGLViFGoxr8Gq2EUiFCIQoIAmiy9wIYLODd\nngDc3sH2XG7/YMHsGV4wa9QK6LUqWBK00GtVUCsVEEUBoihAIQKSBPQP+KBSKtDZ48bRCjs+cPvx\n3++eR5ZVj/l5JszLM8Jk0E7SMxtZWEyHuaAkoazWgfdPNKO83gGvLwilQkBuWjwWzTIj1RKHgM8P\nbZQSEAWcq+lAd58XXX0edPV60epwoaalB0cr7Eg16ZCdHIsUo45rGE8gnz+Io5VtuNjUDZNBi6WF\nZui1arljEdEUplSIeOirhfjpq8fxX6+fxY++NS9iCytJklDX2osPTzXj4/I2eHwBpBp1WL0wHdNT\n4zEtJRaSIOBohX3UbQaDEvoGfOi+NOSib8A3OATD5YWtsx/+wGeV9qGzrSMer1QI0KiViI5SwJqo\nhT5GDb12sMdbr1VBPcqe7gX5ZsRcWj7V7nDhWFUbjle1Y/sH1dj+QTWyrLFYvTAd83gxn3HFYjpM\neXwBHD7Xin3HGmHrdCFOp0ZJYTIKshMwI90w1MtsNOrR3j7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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(height_outlier);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jI9ToieVrisQ" + }, + "source": [ + "Dá para perceber que a maior parte dos dados concentra-se em torno da média (~ 1.7 m) e que apenas algumas observações encontram-se bastante distantes dela." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "q49-oFz4gBHs", + "outputId": "f968b883-a1e3-4ead-963a-19d9f25e9d9e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.7181251474953014, 0.2948590174540895)" + ] + }, + "execution_count": 56, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "height_outlier_mean = height_outlier.mean()\n", + "height_outlier_std = height_outlier.std()\n", + "\n", + "height_outlier_mean, height_outlier_std" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dTtLF6P2rvIh" + }, + "source": [ + "Um jeito de procurar por _outliers_ é ver quem se encontra fora do intervalo $[\\bar{x} - k * \\sigma, \\bar{x} + k * \\sigma]$, onde $k$ geralmente é 1.5, 2.0, 2.5 ou até 3.0.\n", + "\n", + "Abaixo utilizamos o $k = 2$, pois esse valor faz sentido (alturas menores que 1.12 m ou maiores que 2.30 m fogem do nosso padrão):" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "cI8gL-QrgK1s", + "outputId": "6c472ac1-ea23-4dd3-b833-91969a62f92d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.1284071125871225, 2.3078431824034804]" + ] + }, + "execution_count": 57, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "non_outlier_interval_dist = [height_outlier_mean - 2 * height_outlier_std, height_outlier_mean + 2 * height_outlier_std]\n", + "\n", + "non_outlier_interval_dist" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b5A37brPsVPw" + }, + "source": [ + "Novamente, conhecendo o intervalo, podemos identificar as observações que caem foram dele e removê-las:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 104 + }, + "colab_type": "code", + "id": "W6jVe5TMglf5", + "outputId": "c270dcb7-d46a-4dd8-94b3-c3d610269282" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "29 0.516665\n", + "38 2.943781\n", + "48 1.058498\n", + "68 2.737088\n", + "Name: Height, dtype: float64" + ] + }, + "execution_count": 58, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "outliers_dist = height_outlier[(height_outlier < non_outlier_interval_dist[0]) | (height_outlier > non_outlier_interval_dist[1])]\n", + "\n", + "outliers_dist" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "jqYD2d3chJTK" + }, + "outputs": [], + "source": [ + "height_no_outlier_dist = height_outlier.drop(index=outliers_dist.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8IL5fWP1sePM" + }, + "source": [ + "Até agora, nossas métodos de identificação de _outlier_ foram baseadas em estatísticas descritivas do nosso _data set_ (quantis, média e variância). Porém, alguns testes de hipóteses também existem.\n", + "\n", + "Um deles é o teste de Grubb. Esse é um teste bastante simples, cuja estatística de teste $G$ depende dos valores extremos do conjunto e da média amostral:\n", + "\n", + "$$G = \\frac{\\vert x_{\\text{\\{min ou max\\}}} - \\bar{x}\\vert}{s}$$\n", + "\n", + "onde $\\bar{x}$ é a média amostral e $s$ é o desvio-padrão da amostra.\n", + "\n", + "A hipótese nula, $H_{0}$, é de que não existem _outliers_ no _data set_. O teste de Grubb assume que os dados originam-se de uma distribuição normal, então pode ser válido testar essa hipótese antes.\n", + "\n", + "Rejeitamos a hipótese nula se o valor de $G$ encontrado for superior ao valor crítico do teste, que é dado por\n", + "\n", + "$$G_{\\text{crítico}} = \\frac{n - 1}{\\sqrt{n}} \\sqrt{\\frac{t_{\\alpha',n-2}^{2}}{n - 2 + t_{\\alpha',n-2}^{2}}}$$\n", + "\n", + "onde $n$ é o tamanho da amostra, $t$ é um valor com distribuição t-Student e $\\alpha'$ é $\\alpha/2n$ se o teste for bilateral (procuramos _outliers_ muito acima ou muito abaixo) ou $\\alpha/n$ se o teste for unilateral (acreditamos que o _outlier_, se houver, está em somente uma das extremidades da distribuição)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RNveH7ftxMOV" + }, + "source": [ + "Abaixo criamos algumas funções que nos auxiliam nos cálculos e na exibição dos resultados:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Ir61-q0ckV6K" + }, + "outputs": [], + "source": [ + "def grubb_test(g, n, alpha=0.05, tailed='two-tailed'):\n", + " if tailed == 'two-tailed':\n", + " critical = ((n - 1)/sqrt(n)) * sqrt(sct.t.isf(alpha/(2*n), n-2)**2/(n - 2 + sct.t.isf(alpha/(2*n), n-2)**2))\n", + " \n", + " return (g, critical, g > critical)\n", + " elif tailed == 'one-tailed':\n", + " critical = ((n - 1)/sqrt(n)) * sqrt(sct.t.isf(alpha/(n), n-2)**2/(n - 2 + sct.t.isf(alpha/(n), n-2)**2))\n", + " \n", + " return (g, critical, g > critical)\n", + " else:\n", + " raise ValueError(f\"Invalid tailed argument\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "c--VvSPuuHaM" + }, + "outputs": [], + "source": [ + "def grubb_summary(result, decimals=10):\n", + " return (\n", + " f\"Null hypothesis: there is no outliers in the data set\\n\"\n", + " f\"Test statistic: {np.round(result[0], decimals)}, \"\n", + " f\"Grubb's critical value: {np.round(result[1], decimals)}, \"\n", + " f\"Reject: {result[2]}\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "d8nFGEVuqgdC" + }, + "outputs": [], + "source": [ + "def next_outlier_candidate(data):\n", + " sample_distances = (data - data.mean()).abs()\n", + " candidate_idx = sample_distances.idxmax()\n", + " candidate_value = data[candidate_idx]\n", + " candidate_statistic = sample_distances.max()/data.std()\n", + " \n", + " return (candidate_idx, candidate_value, candidate_statistic, len(data))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MRZwuyOOxU7U" + }, + "source": [ + "Ao executarmos o teste de Grubb no nosso conjunto de alturas, encontramos alguns valores onde a hipótese nula é rejeitada, ou seja, há evidência de que o valor extremo é um _outlier_." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 434 + }, + "colab_type": "code", + "id": "Rz-yVWFlt-M6", + "outputId": "cb11e99b-2195-45d7-9089-fdf292a65e1c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index: 38, Value: 2.944, Test statistic: 4.157, Sample size: 100\n", + "\n", + "Null hypothesis: there is no outliers in the data set\n", + "Test statistic: 4.157, Grubb's critical value: 3.384, Reject: True\n", + "\n", + "\n", + "Index: 29, Value: 0.517, Test statistic: 4.421, Sample size: 99\n", + "\n", + "Null hypothesis: there is no outliers in the data set\n", + "Test statistic: 4.421, Grubb's critical value: 3.381, Reject: True\n", + "\n", + "\n", + "Index: 68, Value: 2.737, Test statistic: 4.219, Sample size: 98\n", + "\n", + "Null hypothesis: there is no outliers in the data set\n", + "Test statistic: 4.219, Grubb's critical value: 3.377, Reject: True\n", + "\n", + "\n", + "Index: 48, Value: 1.058, Test statistic: 2.96, Sample size: 97\n", + "\n", + "Null hypothesis: there is no outliers in the data set\n", + "Test statistic: 2.96, Grubb's critical value: 3.374, Reject: False\n", + "\n", + "\n" + ] + } + ], + "source": [ + "height_outlier_grubb = height_outlier.copy()\n", + "outliers_grubb = pd.Series()\n", + "has_outlier = True\n", + "\n", + "while has_outlier:\n", + " outlier_candidate = next_outlier_candidate(height_outlier_grubb)\n", + "\n", + " print(f\"Index: {outlier_candidate[0]}, \"\n", + " f\"Value: {np.round(outlier_candidate[1], 3)}, \"\n", + " f\"Test statistic: {np.round(outlier_candidate[2], 3)}, \"\n", + " f\"Sample size: {outlier_candidate[3]}\\n\")\n", + "\n", + " result = grubb_test(outlier_candidate[2], outlier_candidate[3])\n", + "\n", + " print(grubb_summary(result, 3))\n", + "\n", + " has_outlier = result[2]\n", + "\n", + " if has_outlier:\n", + " height_outlier_grubb = height_outlier_grubb.drop(index=outlier_candidate[0])\n", + " outliers_grubb.at[outlier_candidate[0]] = outlier_candidate[1]\n", + " \n", + " print(f\"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86 + }, + "colab_type": "code", + "id": "49MMneSg-DCj", + "outputId": "a98df152-223e-43e1-ced9-d113a40b879f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "38 2.943781\n", + "29 0.516665\n", + "68 2.737088\n", + "dtype: float64" + ] + }, + "execution_count": 64, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "outliers_grubb" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_hajYam661Zd" + }, + "source": [ + "Abaixo comparamos os _outliers_ encontrados por cada um dos três métodos:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86 + }, + "colab_type": "code", + "id": "l3P2Bavg-zMK", + "outputId": "25065774-49a4-4509-fe92-70a4d32c8cd2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "IQR [29, 38, 48, 68, 91, 92]\n", + "Normal [29, 38, 48, 68]\n", + "Grubb [29, 38, 68]\n", + "dtype: object" + ] + }, + "execution_count": 65, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "outliers = pd.Series({\"IQR\": outliers_iqr.index.values,\n", + " \"Normal\": outliers_dist.index.values,\n", + " \"Grubb\": outliers_grubb.index.values})\n", + "\n", + "outliers.apply(np.sort)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1oMEwGs_DHJW" + }, + "source": [ + "## _Features_ de texto\n", + "\n", + "Dados textuais são muito ricos e muito fáceis de serem encontrados. Diversos _data sets_ são compostos por documentos textuais e ainda um simples _scrapper_ pode coletar dezenas de milhares de documentos da Internet. Coleções de documentos são frequentemente chamadas de _corpus_ (plural, _corpora_).\n", + "\n", + "Nosso objetivo aqui é somente mostrar como preprocessar de forma simples _features_ textuais. Para isso, utilizaremos o _data set_ 20 newsgroups, que contém milhares de documentos categorizados em 20 grupos (desde astronomia até carros)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XItMVwyq8Dp9" + }, + "source": [ + "Abaixo escolhemos somente três grupos para restringir nosso escopo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "usWrDfLvMNxw" + }, + "outputs": [], + "source": [ + "categories = [\"sci.crypt\", \"sci.med\", \"sci.space\"]\n", + "\n", + "newsgroups = fetch_20newsgroups(subset=\"train\", categories=categories, shuffle=True, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4uNwK5uREAn7" + }, + "source": [ + "Temos agora um _corpus_ com 1782 documentos:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "_lUWgt06EtnR", + "outputId": "f82dd8b7-5f76-477c-9173-ee35d0c7e0aa" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1782" + ] + }, + "execution_count": 67, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "len(newsgroups.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "xh326fr28Jyc" + }, + "source": [ + "Um exemplo de documento desse _corpus_ é mostrado abaixo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + }, + "colab_type": "code", + "id": "vsfaD72_M52H", + "outputId": "fb895197-8753-49e6-a631-e7716ad8c8ee" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> Document 4 of 1782:\n", + "\n", + "From: billc@col.hp.com (Bill Claussen)\n", + "Subject: Re: Should I be angry at this doctor?\n", + "Organization: HP Colorado Springs Division\n", + "Lines: 5\n", + "Distribution: na\n", + "NNTP-Posting-Host: hpcspe17.col.hp.com\n", + "\n", + "\n", + "Report them to your local BBB (Better Business Bureau).\n", + "\n", + "Bill Claussen\n", + "\n", + "\n", + "> Category: sci.med\n" + ] + } + ], + "source": [ + "document_idx = 4\n", + "documents_total = len(newsgroups.data)\n", + "\n", + "print(f\"> Document {document_idx} of {documents_total}:\\n\\n{newsgroups.data[document_idx]}\")\n", + "print(f\"> Category: {newsgroups.target_names[newsgroups.target[document_idx]]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6liTZFzv8Nas" + }, + "source": [ + "Quando trabalhando com dados textuais, uma representação simples é ter:\n", + "\n", + "* Cada documento em uma linha.\n", + "* Cada palavra (ou termo) em uma coluna.\n", + "\n", + "Por exemplo, se nosso vocábulário (conjunto de todas palavras ou termos do _corpus_) tiver tamanho 10000 e tivermos 100 documentos, então nosso _data set_ será composto de 100 linhas e 10000 colunas." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qLBi7mFU8mLI" + }, + "source": [ + "O valor de cada célula, $x_{i, j}$, (interseção da linha $i$ com a coluna $j$) do _data set_ depende da tranformação que aplicarmos.\n", + "\n", + "A transformação mais simples é a contagem de palavras no documento, ou seja, $x_{i, j}$ indica o número de ocorrências da palavra $j$ no documento $i$.\n", + "\n", + "Isso pode ser obtido no sklearn pelo `CountVectorizer`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "4E6FmUUhNs8b" + }, + "outputs": [], + "source": [ + "count_vectorizer = CountVectorizer()\n", + "newsgroups_counts = count_vectorizer.fit_transform(newsgroups.data)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "TSylOCPKjLmh", + "outputId": "d7b6e6b8-f227-4ec5-a34a-2cf93fc8ebb5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "scipy.sparse.csr.csr_matrix" + ] + }, + "execution_count": 78, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "type(newsgroups_counts)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "M4rtFrsF9CgR" + }, + "source": [ + "Abaixo escolhemos dez palavras contidas no _corpus_ para exemplificar:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "colab_type": "code", + "id": "kmxzJhkSUpIZ", + "outputId": "613a8241-c25e-4d5d-9830-1cee04671fc4" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " banks business clipper colorado ... kapor monitor private study\n", + "0 0 0 2 0 ... 1 0 0 0\n", + "1 0 0 0 0 ... 0 2 0 0\n", + "2 3 0 0 0 ... 0 0 1 0\n", + "3 0 0 0 0 ... 0 0 0 2\n", + "4 0 1 0 1 ... 0 0 0 0\n", + "\n", + "[5 rows x 10 columns]" + ] + }, + "execution_count": 70, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "words_idx = sorted([count_vectorizer.vocabulary_.get(f\"{word.lower()}\") for word in\n", + " [u\"clipper\", u\"Kapor\",\n", + " u\"monitor\", u\"gibberish\",\n", + " u\"Banks\", u\"private\",\n", + " u\"study\", u\"group\",\n", + " u\"Colorado\", u\"Business\"]])\n", + "\n", + "pd.DataFrame(newsgroups_counts[:5, words_idx].toarray(), columns=np.array(count_vectorizer.get_feature_names())[words_idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C7WuoRgP9WE9" + }, + "source": [ + "Por exemplo, o valor 2 na interseção do documento 0 com a coluna `clipper` indica que a palavra _clipper_ aparece duas vezes no documento 0. Obviamente é possível que uma mesma palavra apareça em múltiplos documentos e mais óbvio ainda que um documento contenha múltiplas palavras." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UQzj-_QT9p7e" + }, + "source": [ + "O problema com essa abordagem é que não temos como medir relevância dos termos. E se o termo é super comum e aparece em quase todos documentos? E se o termo aparece muitas vezes no mesmo documento, mas poucas vezes nos outros?\n", + "\n", + "Essas perguntas não podem ser respondidas simplesmente com a contagem de termos acima. Para isso, precisamos do tf-idf." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AXBnOFk___QK" + }, + "source": [ + "O tf-idf é uma estatística baseada no _corpus_ composta de outras duas estatísticas:\n", + "\n", + "* $\\text{tf}(t, d)$, ou _term frequency_, é uma medida de quantas vezes o termo $t$ aparece no documento $d$. Algumas opções estão disponíveis, mas a mais simples é a contagem do número de ocorrências do termo no documento, $f_{t, d}$, exatamente o que computamos acima. Essa é a forma como sklearn define $tf$:\n", + "\n", + "$$\\text{tf}(t, d) = f_{t, d}$$\n", + "\n", + "* $\\text{idf}(t)$, ou _inverse document frequency_, é uma medida de relevância do termo em todos documentos do _corpus_. O sklearn a computa, seguindo valores _default_, da seguinte forma:\n", + "\n", + "$$\\text{idf}(t) = \\log{\\frac{1+n}{1 + d_{t}}} + 1$$\n", + "\n", + "onde $n$ é o número de documentos no _corpus_ e $d_{t}$ é o número de documentos no _corpus_ que contêm o termo $t$ ($0 < d_{t} \\leq n$).\n", + "\n", + "O tf-idf é calculado multiplicando esses dois valores:\n", + "\n", + "$$\\text{tf-idf}(t, d) = \\text{tf}(t, d) \\times \\text{idf}(t) = f_{t, d} \\times \\log{\\frac{1+n}{1 + d_{t}}} + 1$$\n", + "\n", + "O sklearn também normaliza todos documentos resultantes, ou seja todas linhas da matriz, para terem norma unitária. Em outras palavras, os elementos do vetor de tf-idf do documento $i$ são dados por:\n", + "\n", + "$$\\text{tf-idf}(i, j)_{\\text{normalizado}} = \\frac{\\text{tf-idf}(i, j)}{\\sqrt{\\text{tf-idf}(i, 1)^{2} + \\text{tf-idf}(i, 2)^{2} + \\cdots + \\text{tf-idf}(i, T)^{2}}}$$\n", + "\n", + "onde $T$ é o número de termos do _corpus_, ou seja, o tamanho do vocabulário." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "bWpYWUMjCH8l" + }, + "source": [ + "O tf-idf é sempre um valor não negativo e quanto mais alto, maior a relevância do termo.\n", + "\n", + "Note como o tf aumenta de acordo com o número de ocorrências do termo no documento: quanto mais frequente o termo, mas relevante ele parece ser.\n", + "\n", + "O idf é uma medida de \"raridade\" do termo através de todo _corpus_: quanto mais alto, menos o termo aparece no _corpus_ e consequentemente mais informação ele traz.\n", + "\n", + "Multiplicando os dois, temos uma medida do quão relevante aquele termo é para aquele documento no _corpus_." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b_N2VQnwDaey" + }, + "source": [ + "O sklearn provê um transformador, `TfidfTransformer`, que transforma de uma matriz de frequências, como a retornada pelo `CountVectorizer`, e retorna uma matriz de tf-idf:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Fyxgx0YhVwtF" + }, + "outputs": [], + "source": [ + "tfidf_transformer = TfidfTransformer()\n", + "\n", + "tfidf_transformer.fit(newsgroups_counts)\n", + "\n", + "newsgroups_tfidf = tfidf_transformer.transform(newsgroups_counts)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "colab_type": "code", + "id": "evk8smtLWNtO", + "outputId": "bf99b51a-e276-480c-dee9-13713e85a00b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " banks business clipper ... monitor private study\n", + "0 0.000000 0.000000 0.081293 ... 0.000000 0.000000 0.000000\n", + "1 0.000000 0.000000 0.000000 ... 0.179352 0.000000 0.000000\n", + "2 0.148152 0.000000 0.000000 ... 0.000000 0.048551 0.000000\n", + "3 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.083477\n", + "4 0.000000 0.117248 0.000000 ... 0.000000 0.000000 0.000000\n", + "\n", + "[5 rows x 10 columns]" + ] + }, + "execution_count": 74, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(newsgroups_tfidf_vectorized[:5, words_idx].toarray(), columns=np.array(count_vectorizer.get_feature_names())[words_idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RLFGR7A_D0px" + }, + "source": [ + "Note como a matriz acima é exatamente igual a retornada pelo `TfidfTransformer`.\n", + "\n", + "O resultado (igual da matriz de frequência) é um _data set_ com 1782 documentos e 33796 termos:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "8I_w7yLeYnRe", + "outputId": "e1162574-03a2-4368-c3b6-517759bb973f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1782, 33796)" + ] + }, + "execution_count": 75, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "newsgroups_tfidf_vectorized.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "NjPMTtkUwrS1" + }, + "source": [ + "## Referências\n", + "\n", + "* [Feature engineering](https://jakevdp.github.io/PythonDataScienceHandbook/05.04-feature-engineering.html)\n", + "\n", + "* [Feature Scaling with scikit-learn](http://benalexkeen.com/feature-scaling-with-scikit-learn/)\n", + "\n", + "* [Anthony Goldbloom gives you the secret to winning Kaggle competitions](https://www.import.io/post/how-to-win-a-kaggle-competition/)\n", + "\n", + "* [What are some best practices in Feature Engineering?](https://www.quora.com/What-are-some-best-practices-in-Feature-Engineering)\n", + "\n", + "* [Discover Feature Engineering, How to Engineer Features and How to Get Good at It](https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/)\n", + "\n", + "* [Fundamental Techniques of Feature Engineering for Machine Learning](https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114)\n", + "\n", + "* [Feature Engineering Cookbook for Machine Learning](https://medium.com/@michaelabehsera/feature-engineering-cookbook-for-machine-learning-7bf21f0bcbae)\n", + "\n", + "* [A Simple Guide to Scikit-learn Pipelines](https://medium.com/vickdata/a-simple-guide-to-scikit-learn-pipelines-4ac0d974bdcf)\n", + "\n", + "* [Outlier detection with Scikit Learn](https://www.mikulskibartosz.name/outlier-detection-with-scikit-learn/)\n", + "\n", + "* [Working With Text Data](https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html)\n", + "\n", + "* [WTF is TF-IDF?](https://www.kdnuggets.com/2018/08/wtf-tf-idf.html)\n" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "Aula 7 - Feature Engineering.ipynb", + "provenance": [], + "version": "0.3.2" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Semana 7/aula_7_feature_engineering.ipynb b/Semana 7/aula_7_feature_engineering.ipynb index c7cb165..dc8a2d9 100644 --- a/Semana 7/aula_7_feature_engineering.ipynb +++ b/Semana 7/aula_7_feature_engineering.ipynb @@ -1,5337 +1,5339 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MyaSGq65woLh" + }, + "source": [ + "![Codenation](https://forum.codenation.com.br/uploads/default/original/2X/2/2d2d2a9469f0171e7df2c4ee97f70c555e431e76.png)\n", + "\n", + "__Autor__: Kazuki Yokoyama (kazuki.yokoyama@ufrgs.br)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mi4xZxcfBA2U" + }, + "source": [ + "# _Feature engineering_\n", + "\n", + "![cover](https://venturebeat.com/wp-content/uploads/2018/07/feature_engineering.jpg?resize=680%2C198&strip=all)\n", + "\n", + "Neste módulo, trabalharemos a engenharia de _features_, que consiste em preparar os nossos dados para alimentar os algoritmos de ML adequadamente. Ao contrário do mundo dos tutoriais, na vida real os dados dificilmente estarão prontos para serem consumidos. Grande parte do tempo de um projeto de ML é gasto com a engenharia de _features_, e quanto melhor a qualidade desta etapa, maiores são as chances de melhores resultados nas etapas seguintes." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "cAxxSlo3QrZV" + }, + "source": [ + "## Importação das bibliotecas" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "jMxYy1NkQwW6" + }, + "outputs": [], + "source": [ + "import functools\n", + "from math import sqrt\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import statsmodels.api as sm\n", + "import scipy.stats as sct1\n", + "import seaborn as sns\n", + "from sklearn.datasets import load_digits, fetch_20newsgroups\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.feature_extraction.text import (\n", + " CountVectorizer, TfidfTransformer, TfidfVectorizer\n", + ")\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import (\n", + " OneHotEncoder, Binarizer, KBinsDiscretizer,\n", + " MinMaxScaler, StandardScaler, PolynomialFeatures\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "xNbPRHkKQyv2" + }, + "outputs": [], + "source": [ + "# Algumas configurações para o matplotlib.\n", + "%matplotlib inline\n", + "\n", + "from IPython.core.pylabtools import figsize\n", + "\n", + "\n", + "figsize(12, 12)\n", + "\n", + "sns.set()" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "m8onCO86Q2Hm" + }, + "outputs": [], + "source": [ + "np.random.seed(1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "EIEVdatWDh3Z" + }, + "source": [ + "## _One-hot encoding_\n", + "\n", + "Até aqui, nós praticamente ignoramos a existência de variáveis categóricas. Focamos nas variáveis numéricas porque elas são simples de lidar e bastante comuns. Ainda assim, variáveis categóricas são encontradas facilmente e precisamos de uma forma de trabalhar com elas.\n", + "\n", + "Uma das formas mais simples de representação de variáveis categóricas é através do método chamado _one-hot enconding_. Com ele, uma variável categórica com $h$ categorias é transformada em $h$ novas variáveis binárias (0 ou 1), onde a presença do 1 (_hot_) significa que aquela observação pertence àquela categoria, e 0 (_cold_) que não pertence. Veja um exemplo abaixo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { "colab": { - "name": "Aula 7 - Feature Engineering.ipynb", - "version": "0.3.2", - "provenance": [], - "collapsed_sections": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "M1zv6xPDk4ym", + "outputId": "b9b41a48-556d-44e1-f142-708bae7a2d02" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourse
01.5396.61Biology
11.7646.42Biology
21.6958.95Biology
31.8295.14Biology
41.6406.43Physics
51.7787.98Physics
61.6797.90Biology
71.6046.76Physics
81.8197.44Physics
91.6076.01Physics
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" + ], + "text/plain": [ + " Height Score Course\n", + "0 1.539 6.61 Biology\n", + "1 1.764 6.42 Biology\n", + "2 1.695 8.95 Biology\n", + "3 1.829 5.14 Biology\n", + "4 1.640 6.43 Physics\n", + "5 1.778 7.98 Physics\n", + "6 1.679 7.90 Biology\n", + "7 1.604 6.76 Physics\n", + "8 1.819 7.44 Physics\n", + "9 1.607 6.01 Physics" + ] + }, + "execution_count": 4, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" } + ], + "source": [ + "rows = 100\n", + "\n", + "height = np.random.normal(loc=1.70, scale=0.2, size=rows).round(3)\n", + "score = np.random.normal(loc=7, scale=1, size=rows).round(2)\n", + "courses = [\"Math\", \"Physics\", \"Biology\"]\n", + "course = np.random.choice(courses, size=rows)\n", + "\n", + "data = pd.DataFrame({\"Height\": height, \"Score\": score, \"Course\": course})\n", + "\n", + "data.head(10)" + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "MyaSGq65woLh", - "colab_type": "text" - }, - "source": [ - "![Codenation](https://forum.codenation.com.br/uploads/default/original/2X/2/2d2d2a9469f0171e7df2c4ee97f70c555e431e76.png)\n", - "\n", - "__Autor__: Kazuki Yokoyama (kazuki.yokoyama@ufrgs.br)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mi4xZxcfBA2U", - "colab_type": "text" - }, - "source": [ - "# _Feature engineering_\n", - "\n", - "![cover](https://venturebeat.com/wp-content/uploads/2018/07/feature_engineering.jpg?resize=680%2C198&strip=all)\n", - "\n", - "Neste módulo, trabalharemos a engenharia de _features_, que consiste em preparar os nossos dados para alimentar os algoritmos de ML adequadamente. Ao contrário do mundo dos tutoriais, na vida real os dados dificilmente estarão prontos para serem consumidos. Grande parte do tempo de um projeto de ML é gasto com a engenharia de _features_, e quanto melhor a qualidade desta etapa, maiores são as chances de melhores resultados nas etapas seguintes." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cAxxSlo3QrZV", - "colab_type": "text" - }, - "source": [ - "## Importação das bibliotecas" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "jMxYy1NkQwW6", - "colab_type": "code", - "colab": {} - }, - "source": [ - "import functools\n", - "from math import sqrt\n", - "\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import statsmodels.api as sm\n", - "import scipy.stats as sct\n", - "import seaborn as sns\n", - "from sklearn.datasets import load_digits, fetch_20newsgroups\n", - "from sklearn.decomposition import PCA\n", - "from sklearn.feature_extraction.text import (\n", - " CountVectorizer, TfidfTransformer, TfidfVectorizer\n", - ")\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.preprocessing import (\n", - " OneHotEncoder, Binarizer, KBinsDiscretizer,\n", - " MinMaxScaler, StandardScaler, PolynomialFeatures\n", - ")" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "xNbPRHkKQyv2", - "colab_type": "code", - "colab": {} - }, - "source": [ - "# Algumas configurações para o matplotlib.\n", - "%matplotlib inline\n", - "\n", - "from IPython.core.pylabtools import figsize\n", - "\n", - "\n", - "figsize(12, 12)\n", - "\n", - "sns.set()" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "m8onCO86Q2Hm", - "colab_type": "code", - "colab": {} - }, - "source": [ - "np.random.seed(1000)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EIEVdatWDh3Z", - "colab_type": "text" - }, - "source": [ - "## _One-hot encoding_\n", - "\n", - "Até aqui, nós praticamente ignoramos a existência de variáveis categóricas. Focamos nas variáveis numéricas porque elas são simples de lidar e bastante comuns. Ainda assim, variáveis categóricas são encontradas facilmente e precisamos de uma forma de trabalhar com elas.\n", - "\n", - "Uma das formas mais simples de representação de variáveis categóricas é através do método chamado _one-hot enconding_. Com ele, uma variável categórica com $h$ categorias é transformada em $h$ novas variáveis binárias (0 ou 1), onde a presença do 1 (_hot_) significa que aquela observação pertence àquela categoria, e 0 (_cold_) que não pertence. Veja um exemplo abaixo:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "M1zv6xPDk4ym", - "colab_type": "code", - "outputId": "b9b41a48-556d-44e1-f142-708bae7a2d02", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "source": [ - "rows = 100\n", - "\n", - "height = np.random.normal(loc=1.70, scale=0.2, size=rows).round(3)\n", - "score = np.random.normal(loc=7, scale=1, size=rows).round(2)\n", - "courses = [\"Math\", \"Physics\", \"Biology\"]\n", - "course = np.random.choice(courses, size=rows)\n", - "\n", - "data = pd.DataFrame({\"Height\": height, \"Score\": score, \"Course\": course})\n", - "\n", - "data.head(10)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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HeightScoreCourse
01.5396.61Biology
11.7646.42Biology
21.6958.95Biology
31.8295.14Biology
41.6406.43Physics
51.7787.98Physics
61.6797.90Biology
71.6046.76Physics
81.8197.44Physics
91.6076.01Physics
\n", - "
" - ], - "text/plain": [ - " Height Score Course\n", - "0 1.539 6.61 Biology\n", - "1 1.764 6.42 Biology\n", - "2 1.695 8.95 Biology\n", - "3 1.829 5.14 Biology\n", - "4 1.640 6.43 Physics\n", - "5 1.778 7.98 Physics\n", - "6 1.679 7.90 Biology\n", - "7 1.604 6.76 Physics\n", - "8 1.819 7.44 Physics\n", - "9 1.607 6.01 Physics" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 4 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nK_6LysZP6Lw", - "colab_type": "text" - }, - "source": [ - "Criamos um _data set_ que contém duas variáveis numéricas (`Height` e `Score`) e uma variável categórica (`Course`). Nosso objetivo com o _one-hot encoding_ é transformar a variável `Course` em uma sequência de variáveis numéricas binárias, cada uma descrevendo uma classe da variável. Neste caso, como temos três categorias para `Course` (Biology, Physics e Math), teremos três novas variáveis binárias.\n", - "\n", - "Vamos treinar esse _encoder_:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "MDpY6XcNmYlw", - "colab_type": "code", - "outputId": "5fda81c9-000d-4557-cb3f-22d012b3e548", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - } - }, - "source": [ - "one_hot_encoder = OneHotEncoder(sparse=False, dtype=np.int)\n", - "\n", - "#one_hot_encoder.fit(data[[\"Course\"]])\n", - "\n", - "#course_encoded = one_hot_encoder.transform(...)\n", - "\n", - "course_encoded = one_hot_encoder.fit_transform(data[[\"Course\"]])\n", - "\n", - "course_encoded[:10]" + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nK_6LysZP6Lw" + }, + "source": [ + "Criamos um _data set_ que contém duas variáveis numéricas (`Height` e `Score`) e uma variável categórica (`Course`). Nosso objetivo com o _one-hot encoding_ é transformar a variável `Course` em uma sequência de variáveis numéricas binárias, cada uma descrevendo uma classe da variável. Neste caso, como temos três categorias para `Course` (Biology, Physics e Math), teremos três novas variáveis binárias.\n", + "\n", + "Vamos treinar esse _encoder_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "MDpY6XcNmYlw", + "outputId": "5fda81c9-000d-4557-cb3f-22d012b3e548" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 0, 0],\n", + " [1, 0, 0],\n", + " [1, 0, 0],\n", + " [1, 0, 0],\n", + " [0, 0, 1],\n", + " [0, 0, 1],\n", + " [1, 0, 0],\n", + " [0, 0, 1],\n", + " [0, 0, 1],\n", + " [0, 0, 1]])" + ] + }, + "execution_count": 5, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "one_hot_encoder = OneHotEncoder(sparse=False, dtype=np.int)\n", + "\n", + "#one_hot_encoder.fit(data[[\"Course\"]])\n", + "\n", + "#course_encoded = one_hot_encoder.transform(...)\n", + "\n", + "course_encoded = one_hot_encoder.fit_transform(data[[\"Course\"]])\n", + "\n", + "course_encoded[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "V-O0cMCyQqk4" + }, + "source": [ + "A saída é um `np.ndarray` com formato `(n, h)`, onde `n` é o número de observações no _data set_ e `h` é o número de categorias da variável codificada." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "BP_QsDI6REl_", + "outputId": "10a0faf0-b05f-4ad8-f79d-7642d15862a7" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(100, 3)" + ] + }, + "execution_count": 6, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "course_encoded.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eoRT2AR8RHNl" + }, + "source": [ + "No atributo `categories_` do _encoder_, temos as categorias da variável:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "ziGE3VCinqM7", + "outputId": "2c77ac8b-ba1b-4479-97aa-b59cff8b78bf" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[array(['Biology', 'Math', 'Physics'], dtype=object)]" + ] + }, + "execution_count": 7, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "one_hot_encoder.categories_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "y8V2WMjmRUkw" + }, + "source": [ + "Podemos criar as novas colunas que descrevem cada categoria. Repare que, para qualquer linha, apenas uma das colunas contém um 1, indicando a qual categoria aquela observação pertence. Isso acontece, obviamente, se as categorias forem mutuamente exclusivas (uma observação não pode pertencer a mais de uma categoria simultaneamente)." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "dGepWPRFoqc0", + "outputId": "dc6a6dff-007d-4f66-cbfb-2aad4c8a7448" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourseBiologyMathPhysics
01.5396.61Biology100
11.7646.42Biology100
21.6958.95Biology100
31.8295.14Biology100
41.6406.43Physics001
51.7787.98Physics001
61.6797.90Biology100
71.6046.76Physics001
81.8197.44Physics001
91.6076.01Physics001
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" ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[1, 0, 0],\n", - " [1, 0, 0],\n", - " [1, 0, 0],\n", - " [1, 0, 0],\n", - " [0, 0, 1],\n", - " [0, 0, 1],\n", - " [1, 0, 0],\n", - " [0, 0, 1],\n", - " [0, 0, 1],\n", - " [0, 0, 1]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 5 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "V-O0cMCyQqk4", - "colab_type": "text" - }, - "source": [ - "A saída é um `np.ndarray` com formato `(n, h)`, onde `n` é o número de observações no _data set_ e `h` é o número de categorias da variável codificada." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "BP_QsDI6REl_", - "colab_type": "code", - "outputId": "10a0faf0-b05f-4ad8-f79d-7642d15862a7", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "course_encoded.shape" + "text/plain": [ + " Height Score Course Biology Math Physics\n", + "0 1.539 6.61 Biology 1 0 0\n", + "1 1.764 6.42 Biology 1 0 0\n", + "2 1.695 8.95 Biology 1 0 0\n", + "3 1.829 5.14 Biology 1 0 0\n", + "4 1.640 6.43 Physics 0 0 1\n", + "5 1.778 7.98 Physics 0 0 1\n", + "6 1.679 7.90 Biology 1 0 0\n", + "7 1.604 6.76 Physics 0 0 1\n", + "8 1.819 7.44 Physics 0 0 1\n", + "9 1.607 6.01 Physics 0 0 1" + ] + }, + "execution_count": 8, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "columns_encoded = one_hot_encoder.categories_[0]\n", + "\n", + "data_encoded = pd.concat([data, pd.DataFrame(course_encoded, columns=columns_encoded)], axis=1)\n", + "\n", + "data_encoded.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iIiVR7P4SHXz" + }, + "source": [ + "Como você deve imaginar, a maior parte da matriz retornada é composta por zeros, sendo apenas alguns elementos compostos de um. Dizemos que essa matriz é __esparsa__. É um grande desperdício de memória trabalhar diretamente como uma matriz esparsa assim. Por isso, o _default_ do `OneHotEncoder` é retornar uma `sparse matrix` do NumPy, economizando espaço em memória:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "colab_type": "code", + "id": "muGSmJckraf3", + "outputId": "c8957d2b-68c4-4722-80ea-5e241c479a88" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<100x3 sparse matrix of type ''\n", + "\twith 100 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 9, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "one_hot_encoder_sparse = OneHotEncoder(sparse=True) # sparse=True é o default.\n", + "\n", + "course_encoded_sparse = one_hot_encoder_sparse.fit_transform(data[[\"Course\"]])\n", + "\n", + "course_encoded_sparse" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "FOYl0Lx8TPJm" + }, + "source": [ + "Para acessar os dados dessa matriz, podemos convertê-la para um _array_ não esparso:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "mtUziaQmrqTN", + "outputId": "bb7920ae-69a0-4543-97da-b1fc2746ddd0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [1., 0., 0.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.]])" + ] + }, + "execution_count": 10, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "course_encoded_sparse.toarray()[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "zHGmVXu1uEvM" + }, + "source": [ + "## Binarização (_Binarization_)\n", + "\n", + "Binarização é o processo de discretizar uma variável numérica em dois níveis com base em um _threshold_. Isso pode ser útil, por exemplo, para tornar uma variável numérica contínua em uma variável binária alvo de duas classes (positiva ou negativa).\n", + "\n", + "No exemplo abaixo, vamos separar a variável `Height` em dois grupos, utilizando 1.80 m como _threshold_ de separação. Observações que possuam menos de 1.80 m terão valor 0, enquanto aquelas com mais de 1.80 m terão valor 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 208 + }, + "colab_type": "code", + "id": "PeGrPpyWPcOw", + "outputId": "edb6b4c4-97e9-4914-f952-aa60c6dbbbc2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 True\n", + "4 False\n", + "5 False\n", + "6 False\n", + "7 False\n", + "8 True\n", + "9 False\n", + "Name: Height, dtype: bool" + ] + }, + "execution_count": 11, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "tall = (data_encoded.Height > 1.80)\n", + "\n", + "tall[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "94vcsMVguGvG", + "outputId": "b2b15447-7399-4309-b18a-3de5a183a41e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.],\n", + " [0.],\n", + " [0.],\n", + " [1.],\n", + " [0.],\n", + " [0.],\n", + " [0.],\n", + " [0.],\n", + " [1.],\n", + " [0.]])" + ] + }, + "execution_count": 12, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "binarizer = Binarizer(threshold=1.80).fit(data_encoded[[\"Height\"]])\n", + "\n", + "height_binary = binarizer.transform(data_encoded[[\"Height\"]])\n", + "\n", + "height_binary[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "oND_xnxRV8wZ" + }, + "source": [ + "O `Binarizer` tem como saída uma matriz binária numérica. Podemos transformá-la em um vetor de _bool_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "iXbf50-4vdDR", + "outputId": "2f7dba40-f513-491a-e072-743ac0a8c88f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(100, 3)" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 6 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "eoRT2AR8RHNl", - "colab_type": "text" - }, - "source": [ - "No atributo `categories_` do _encoder_, temos as categorias da variável:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "ziGE3VCinqM7", - "colab_type": "code", - "outputId": "2c77ac8b-ba1b-4479-97aa-b59cff8b78bf", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "one_hot_encoder.categories_" + "text/plain": [ + " Tall\n", + "0 False\n", + "1 False\n", + "2 False\n", + "3 True\n", + "4 False\n", + "5 False\n", + "6 False\n", + "7 False\n", + "8 True\n", + "9 False" + ] + }, + "execution_count": 13, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "height_bool = pd.DataFrame(height_binary.flatten().astype(bool), columns=[\"Tall\"])\n", + "\n", + "height_bool.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nn9Gs9DhWNvi" + }, + "source": [ + "Vamos adicionar a nova variável `Tall`, que indica se a pessoa é alta (> 1.80 m), ao nosso _data set_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "xjOV0WlJy7DY", + "outputId": "af316c4b-4931-44cb-a4af-4fa51b3c93fc" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourseBiologyMathPhysicsTall
01.5396.61Biology100False
11.7646.42Biology100False
21.6958.95Biology100False
31.8295.14Biology100True
41.6406.43Physics001False
51.7787.98Physics001False
61.6797.90Biology100False
71.6046.76Physics001False
81.8197.44Physics001True
91.6076.01Physics001False
\n", + "
" ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[array(['Biology', 'Math', 'Physics'], dtype=object)]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 7 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "y8V2WMjmRUkw", - "colab_type": "text" - }, - "source": [ - "Podemos criar as novas colunas que descrevem cada categoria. Repare que, para qualquer linha, apenas uma das colunas contém um 1, indicando a qual categoria aquela observação pertence. Isso acontece, obviamente, se as categorias forem mutuamente exclusivas (uma observação não pode pertencer a mais de uma categoria simultaneamente)." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "dGepWPRFoqc0", - "colab_type": "code", - "outputId": "dc6a6dff-007d-4f66-cbfb-2aad4c8a7448", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "source": [ - "columns_encoded = one_hot_encoder.categories_[0]\n", - "\n", - "data_encoded = pd.concat([data, pd.DataFrame(course_encoded, columns=columns_encoded)], axis=1)\n", - "\n", - "data_encoded.head(10)" + "text/plain": [ + " Height Score Course Biology Math Physics Tall\n", + "0 1.539 6.61 Biology 1 0 0 False\n", + "1 1.764 6.42 Biology 1 0 0 False\n", + "2 1.695 8.95 Biology 1 0 0 False\n", + "3 1.829 5.14 Biology 1 0 0 True\n", + "4 1.640 6.43 Physics 0 0 1 False\n", + "5 1.778 7.98 Physics 0 0 1 False\n", + "6 1.679 7.90 Biology 1 0 0 False\n", + "7 1.604 6.76 Physics 0 0 1 False\n", + "8 1.819 7.44 Physics 0 0 1 True\n", + "9 1.607 6.01 Physics 0 0 1 False" + ] + }, + "execution_count": 14, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_encoded = pd.concat([data_encoded, height_bool], axis=1)\n", + "\n", + "data_encoded.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2tOdmnNi23p4" + }, + "source": [ + "## Discretização (_Binning_)\n", + "\n", + "Discretização, como o nome diz, é o processo de discretizar ou separar em intervalos contínuos uma variável numérica. Isso pode ser útil para converter uma variável numérica em categórica, quando o valor exato numérico não for tão importante quanto o intervalo onde ele se encontra.\n", + "\n", + "Podemos criar _bins_ (_buckets_ ou intervalos) que contenham aproximadamente a mesma quantidade de observações, utilizando a estratégia `quantile` ou que sejam igualmente espaçados com a estratégia `uniform`.\n", + "\n", + "No exemplo a seguir, criamos quatro intervalos da variável `Score` com a estratégia `quantile`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "Xir4K6i522ZQ", + "outputId": "e902850a-d3dc-4d97-a80f-ad3dad1bb1a2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.],\n", + " [1.],\n", + " [3.],\n", + " [0.],\n", + " [1.],\n", + " [3.],\n", + " [3.],\n", + " [2.],\n", + " [2.],\n", + " [0.]])" + ] + }, + "execution_count": 15, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "discretizer = KBinsDiscretizer(n_bins=4, encode=\"ordinal\", strategy=\"quantile\")\n", + "\n", + "discretizer.fit(data_encoded[[\"Score\"]])\n", + "\n", + "score_bins = discretizer.transform(data_encoded[[\"Score\"]])\n", + "\n", + "score_bins[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3hrP6E4xYXCs" + }, + "source": [ + "Os limites dos intervalos estão disponíveis no atributo `bin_edges_`. Isso pode ser útil para criarmos _labels_ para colunas do _data set_ por exemplo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "ScCmeNtn3-fF", + "outputId": "be1003a5-2d28-42d6-e76d-bc349e957e95" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([array([4.09 , 6.1975, 6.735 , 7.6 , 9.28 ])], dtype=object)" + ] + }, + "execution_count": 16, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "discretizer.bin_edges_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vGl5ONq2Yk7r" + }, + "source": [ + "A função `get_interval()` abaixo facilita a criação de _labels_ indicativas dos intervalos:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "fvB70_vd4fSO" + }, + "outputs": [], + "source": [ + "def get_interval(bin_idx, bin_edges):\n", + " return f\"{np.round(bin_edges[bin_idx], 2):.2f} ⊢ {np.round(bin_edges[bin_idx+1], 2):.2f}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Hn3eqHFbYtfm" + }, + "source": [ + "Cada um dos intervalos mostrados abaixo deve possuir aproximadamente a mesma quantidade de observações:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "colab_type": "code", + "id": "HX59pepN5ZQQ", + "outputId": "d5b3d4dc-c969-44cb-fa34-e31fad2dd818" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bins quantile\n", + "interval: #elements\n", + "\n", + "4.09 ⊢ 6.20: 25\n", + "6.20 ⊢ 6.74: 25\n", + "6.74 ⊢ 7.60: 25\n", + "7.60 ⊢ 9.28: 25\n" + ] + } + ], + "source": [ + "bin_edges_quantile = discretizer.bin_edges_[0]\n", + "\n", + "print(f\"Bins quantile\")\n", + "print(f\"interval: #elements\\n\")\n", + "for i in range(len(discretizer.bin_edges_[0])-1):\n", + " print(f\"{get_interval(i, bin_edges_quantile)}: {sum(score_bins[:, 0] == i)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OQ0fli3IY2G6" + }, + "source": [ + "A _Series_ abaixo mostra alguns dos intervalos para os quais as observações foram encaixadas:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 208 + }, + "colab_type": "code", + "id": "SZMBYjqR5-H6", + "outputId": "cba541dc-9f9e-48d8-eb87-fa54440ca353" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 6.20 ⊢ 6.74\n", + "1 6.20 ⊢ 6.74\n", + "2 7.60 ⊢ 9.28\n", + "3 4.09 ⊢ 6.20\n", + "4 6.20 ⊢ 6.74\n", + "5 7.60 ⊢ 9.28\n", + "6 7.60 ⊢ 9.28\n", + "7 6.74 ⊢ 7.60\n", + "8 6.74 ⊢ 7.60\n", + "9 4.09 ⊢ 6.20\n", + "dtype: object" + ] + }, + "execution_count": 19, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "score_intervals = pd.Series(score_bins.flatten().astype(np.int)).apply(get_interval, args=(bin_edges_quantile,))\n", + "\n", + "score_intervals.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6gWE7IU6Y_9q" + }, + "source": [ + "Também podemos criar uma nova variável, `Score_interval`, no nosso _data set_ com os intervalos (que agora são categorias):" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "fomFOQbVA8eS", + "outputId": "1f065c4f-6da4-43ad-ebb7-b58706595871" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourseBiologyMathPhysicsTallScore_interval
01.5396.61Biology100False6.20 ⊢ 6.74
11.7646.42Biology100False6.20 ⊢ 6.74
21.6958.95Biology100False7.60 ⊢ 9.28
31.8295.14Biology100True4.09 ⊢ 6.20
41.6406.43Physics001False6.20 ⊢ 6.74
51.7787.98Physics001False7.60 ⊢ 9.28
61.6797.90Biology100False7.60 ⊢ 9.28
71.6046.76Physics001False6.74 ⊢ 7.60
81.8197.44Physics001True6.74 ⊢ 7.60
91.6076.01Physics001False4.09 ⊢ 6.20
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HeightScoreCourseBiologyMathPhysics
01.5396.61Biology100
11.7646.42Biology100
21.6958.95Biology100
31.8295.14Biology100
41.6406.43Physics001
51.7787.98Physics001
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71.6046.76Physics001
81.8197.44Physics001
91.6076.01Physics001
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" - ], - "text/plain": [ - " Height Score Course Biology Math Physics\n", - "0 1.539 6.61 Biology 1 0 0\n", - "1 1.764 6.42 Biology 1 0 0\n", - "2 1.695 8.95 Biology 1 0 0\n", - "3 1.829 5.14 Biology 1 0 0\n", - "4 1.640 6.43 Physics 0 0 1\n", - "5 1.778 7.98 Physics 0 0 1\n", - "6 1.679 7.90 Biology 1 0 0\n", - "7 1.604 6.76 Physics 0 0 1\n", - "8 1.819 7.44 Physics 0 0 1\n", - "9 1.607 6.01 Physics 0 0 1" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 8 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iIiVR7P4SHXz", - "colab_type": "text" - }, - "source": [ - "Como você deve imaginar, a maior parte da matriz retornada é composta por zeros, sendo apenas alguns elementos compostos de um. Dizemos que essa matriz é __esparsa__. É um grande desperdício de memória trabalhar diretamente como uma matriz esparsa assim. Por isso, o _default_ do `OneHotEncoder` é retornar uma `sparse matrix` do NumPy, economizando espaço em memória:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "muGSmJckraf3", - "colab_type": "code", - "outputId": "c8957d2b-68c4-4722-80ea-5e241c479a88", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 52 - } - }, - "source": [ - "one_hot_encoder_sparse = OneHotEncoder(sparse=True) # sparse=True é o default.\n", - "\n", - "course_encoded_sparse = one_hot_encoder_sparse.fit_transform(data[[\"Course\"]])\n", - "\n", - "course_encoded_sparse" + "text/plain": [ + " Height Score Course Biology Math Physics Tall Score_interval\n", + "0 1.539 6.61 Biology 1 0 0 False 6.20 ⊢ 6.74\n", + "1 1.764 6.42 Biology 1 0 0 False 6.20 ⊢ 6.74\n", + "2 1.695 8.95 Biology 1 0 0 False 7.60 ⊢ 9.28\n", + "3 1.829 5.14 Biology 1 0 0 True 4.09 ⊢ 6.20\n", + "4 1.640 6.43 Physics 0 0 1 False 6.20 ⊢ 6.74\n", + "5 1.778 7.98 Physics 0 0 1 False 7.60 ⊢ 9.28\n", + "6 1.679 7.90 Biology 1 0 0 False 7.60 ⊢ 9.28\n", + "7 1.604 6.76 Physics 0 0 1 False 6.74 ⊢ 7.60\n", + "8 1.819 7.44 Physics 0 0 1 True 6.74 ⊢ 7.60\n", + "9 1.607 6.01 Physics 0 0 1 False 4.09 ⊢ 6.20" + ] + }, + "execution_count": 20, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_encoded = pd.concat([data_encoded, pd.DataFrame(score_intervals, columns=[\"Score_interval\"])], axis=1)\n", + "\n", + "data_encoded.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LldlZ92lZN1k" + }, + "source": [ + "Como dito, podemos utilizar a estratégia `uniform` para criar _bins_ igualmente espaçados, independente do número de observações que cada um possui. Também podemos especificar o tipo de codificação utilizada. No caso a seguir, utilizamos `encode=onehot-dense` para informar que queremos que a saída seja codificada como o _one-hot encode_ visto anteriormente:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "E6L1qXuW-v-n", + "outputId": "956f9e9f-67ba-436f-f457-889ee2d1f3db" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 0, 0],\n", + " [0, 1, 0, 0],\n", + " [0, 0, 0, 1],\n", + " [1, 0, 0, 0],\n", + " [0, 1, 0, 0],\n", + " [0, 0, 1, 0],\n", + " [0, 0, 1, 0],\n", + " [0, 0, 1, 0],\n", + " [0, 0, 1, 0],\n", + " [0, 1, 0, 0]])" + ] + }, + "execution_count": 21, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "discretizer_uniform = KBinsDiscretizer(n_bins=4, encode=\"onehot-dense\", strategy=\"uniform\")\n", + "\n", + "discretizer_uniform.fit(data_encoded[[\"Score\"]])\n", + "\n", + "score_bins_uniform = discretizer_uniform.transform(data_encoded[[\"Score\"]]).astype(np.int)\n", + "\n", + "score_bins_uniform[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "YapI8RuMZZfM" + }, + "source": [ + "Note como agora os intervalos são ligeiramente diferentes:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "P8gW9k-w-_CC", + "outputId": "731fca86-f052-4a93-e5bf-e13eec18ac8b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4.09 , 5.3875, 6.685 , 7.9825, 9.28 ])" + ] + }, + "execution_count": 22, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "bin_edges_uniform = discretizer_uniform.bin_edges_[0]\n", + "\n", + "bin_edges_uniform" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "colab_type": "code", + "id": "ieyy46EJAnb6", + "outputId": "99835fa9-8003-4060-afae-2c4de66685ff" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bins uniform\n", + "interval: #elements\n", + "\n", + "4.09 ⊢ 5.39: 6\n", + "5.39 ⊢ 6.68: 43\n", + "6.68 ⊢ 7.98: 44\n", + "7.98 ⊢ 9.28: 7\n" + ] + } + ], + "source": [ + "score_intervals_columns = [get_interval(i, bin_edges_uniform) for i in range(4)]\n", + "\n", + "print(f\"Bins uniform\")\n", + "print(f\"interval: #elements\\n\")\n", + "for i in range(len(discretizer_uniform.bin_edges_[0])-1):\n", + " print(f\"{get_interval(i, bin_edges_uniform)}: {sum(score_bins_uniform[:, i])}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "WuWi-1U4Zzf_" + }, + "source": [ + "Podemos adicionar as novas variáveis binárias no _data set_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "P-v3UgiQB87S", + "outputId": "ad22d68f-c0e8-4a91-8838-842e7e2f5041" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourseBiologyMathPhysicsTallScore_interval4.09 ⊢ 5.395.39 ⊢ 6.686.68 ⊢ 7.987.98 ⊢ 9.28
01.5396.61Biology100False6.20 ⊢ 6.740100
11.7646.42Biology100False6.20 ⊢ 6.740100
21.6958.95Biology100False7.60 ⊢ 9.280001
31.8295.14Biology100True4.09 ⊢ 6.201000
41.6406.43Physics001False6.20 ⊢ 6.740100
51.7787.98Physics001False7.60 ⊢ 9.280010
61.6797.90Biology100False7.60 ⊢ 9.280010
71.6046.76Physics001False6.74 ⊢ 7.600010
81.8197.44Physics001True6.74 ⊢ 7.600010
91.6076.01Physics001False4.09 ⊢ 6.200100
\n", + "
" ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "<100x3 sparse matrix of type ''\n", - "\twith 100 stored elements in Compressed Sparse Row format>" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 9 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FOYl0Lx8TPJm", - "colab_type": "text" - }, - "source": [ - "Para acessar os dados dessa matriz, podemos convertê-la para um _array_ não esparso:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "mtUziaQmrqTN", - "colab_type": "code", - "outputId": "bb7920ae-69a0-4543-97da-b1fc2746ddd0", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - } - }, - "source": [ - "course_encoded_sparse.toarray()[:10]" + "text/plain": [ + " Height Score Course ... 5.39 ⊢ 6.68 6.68 ⊢ 7.98 7.98 ⊢ 9.28\n", + "0 1.539 6.61 Biology ... 1 0 0\n", + "1 1.764 6.42 Biology ... 1 0 0\n", + "2 1.695 8.95 Biology ... 0 0 1\n", + "3 1.829 5.14 Biology ... 0 0 0\n", + "4 1.640 6.43 Physics ... 1 0 0\n", + "5 1.778 7.98 Physics ... 0 1 0\n", + "6 1.679 7.90 Biology ... 0 1 0\n", + "7 1.604 6.76 Physics ... 0 1 0\n", + "8 1.819 7.44 Physics ... 0 1 0\n", + "9 1.607 6.01 Physics ... 1 0 0\n", + "\n", + "[10 rows x 12 columns]" + ] + }, + "execution_count": 24, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_encoded = pd.concat([data_encoded, pd.DataFrame(score_bins_uniform, columns=score_intervals_columns)], axis=1)\n", + "\n", + "data_encoded.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jD8WM_-yzqSc" + }, + "source": [ + "## Normalização (_Scaling_)\n", + "\n", + "Normalização é o processo de colocar uma variável numérica em uma escala pré-determinada, geralmente $[0, 1]$, mas também é comum ser $[-1, 1]$.\n", + "\n", + "Para colocar no intervalo $[0, 1]$, basta subtrair cada valor da valor mínimo e dividir pela diferença do valor máximo e mínimo:\n", + "\n", + "$$x_{\\text{scaled}} = \\frac{x - x_{\\text{min}}}{x_{\\text{max}} - x_{\\text{min}}}$$\n", + "\n", + "Abaixo, escalamos a variável `Score` no intervalo $[0, 1]$:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "nMM2mu-Qzwnv", + "outputId": "5c60c83b-13bf-431d-e77e-a2fb2e8af317" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.48554913],\n", + " [0.44894027],\n", + " [0.93641618],\n", + " [0.20231214],\n", + " [0.45086705],\n", + " [0.7495183 ],\n", + " [0.73410405],\n", + " [0.51445087],\n", + " [0.64547206],\n", + " [0.3699422 ]])" + ] + }, + "execution_count": 25, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "minmax_scaler = MinMaxScaler(feature_range=(0, 1)) # Default feature_scale é (0, 1).\n", + "\n", + "minmax_scaler.fit(data_encoded[[\"Score\"]])\n", + "\n", + "score_normalized = minmax_scaler.transform(data_encoded[[\"Score\"]])\n", + "\n", + "score_normalized[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "FPr-37M2UBj4", + "outputId": "dc170301-56af-4cab-da7c-307c5cbb94a6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 0.9999999999999999)" + ] + }, + "execution_count": 26, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "score_normalized.min(), score_normalized.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Et6m_2Bbbq-n" + }, + "source": [ + "Adicionamos a variável `Score` normalizada ao nosso _data set_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "kaYvCQtK0fzi", + "outputId": "9f8ccb6c-d0b7-4445-96c9-490f284f2357" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourseBiologyMathPhysicsTallScore_interval4.09 ⊢ 5.395.39 ⊢ 6.686.68 ⊢ 7.987.98 ⊢ 9.28Score_normalized
01.5396.61Biology100False6.20 ⊢ 6.7401000.485549
11.7646.42Biology100False6.20 ⊢ 6.7401000.448940
21.6958.95Biology100False7.60 ⊢ 9.2800010.936416
31.8295.14Biology100True4.09 ⊢ 6.2010000.202312
41.6406.43Physics001False6.20 ⊢ 6.7401000.450867
51.7787.98Physics001False7.60 ⊢ 9.2800100.749518
61.6797.90Biology100False7.60 ⊢ 9.2800100.734104
71.6046.76Physics001False6.74 ⊢ 7.6000100.514451
81.8197.44Physics001True6.74 ⊢ 7.6000100.645472
91.6076.01Physics001False4.09 ⊢ 6.2001000.369942
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" ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[1., 0., 0.],\n", - " [1., 0., 0.],\n", - " [1., 0., 0.],\n", - " [1., 0., 0.],\n", - " [0., 0., 1.],\n", - " [0., 0., 1.],\n", - " [1., 0., 0.],\n", - " [0., 0., 1.],\n", - " [0., 0., 1.],\n", - " [0., 0., 1.]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 10 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zHGmVXu1uEvM", - "colab_type": "text" - }, - "source": [ - "## Binarização (_Binarization_)\n", - "\n", - "Binarização é o processo de discretizar uma variável numérica em dois níveis com base em um _threshold_. Isso pode ser útil, por exemplo, para tornar uma variável numérica contínua em uma variável binária alvo de duas classes (positiva ou negativa).\n", - "\n", - "No exemplo abaixo, vamos separar a variável `Height` em dois grupos, utilizando 1.80 m como _threshold_ de separação. Observações que possuam menos de 1.80 m terão valor 0, enquanto aquelas com mais de 1.80 m terão valor 1:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "PeGrPpyWPcOw", - "colab_type": "code", - "outputId": "edb6b4c4-97e9-4914-f952-aa60c6dbbbc2", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 208 - } - }, - "source": [ - "tall = (data_encoded.Height > 1.80)\n", - "\n", - "tall[:10]" + "text/plain": [ + " Height Score Course ... 6.68 ⊢ 7.98 7.98 ⊢ 9.28 Score_normalized\n", + "0 1.539 6.61 Biology ... 0 0 0.485549\n", + "1 1.764 6.42 Biology ... 0 0 0.448940\n", + "2 1.695 8.95 Biology ... 0 1 0.936416\n", + "3 1.829 5.14 Biology ... 0 0 0.202312\n", + "4 1.640 6.43 Physics ... 0 0 0.450867\n", + "5 1.778 7.98 Physics ... 1 0 0.749518\n", + "6 1.679 7.90 Biology ... 1 0 0.734104\n", + "7 1.604 6.76 Physics ... 1 0 0.514451\n", + "8 1.819 7.44 Physics ... 1 0 0.645472\n", + "9 1.607 6.01 Physics ... 0 0 0.369942\n", + "\n", + "[10 rows x 13 columns]" + ] + }, + "execution_count": 27, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_encoded = pd.concat([data_encoded, pd.DataFrame(score_normalized.flatten(), columns=[\"Score_normalized\"])], axis=1)\n", + "\n", + "data_encoded.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "n7-msElsbveR" + }, + "source": [ + "Para avaliar se os valores encontrados conferem, podemos utilizar a função `normalize` abaixo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "EAfUGaFc061d" + }, + "outputs": [], + "source": [ + "def normalize(x, xmin, xmax):\n", + " return (x - xmin)/(xmax - xmin)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "CXywxNX-b-0K" + }, + "source": [ + "A função `partial()` do módulo `functools` (_builtin_ do Python) permite \"congelar\" alguns parâmetros da função passaga como argumento, facilitando a invocação desta função quando tais parâmetros são constantes. No caso abaixo, \"congelamos\" os argumentos `xmin` e `xmax` da função `normalize()` com os valores mínimo e máximo da variável `Score`, respectivamente. Nas invocações subsequentes de `normalize` não precisaremos passar esses argumentos, somente o argumento \"não congelado\" `x`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "UAlpigp21OVx" + }, + "outputs": [], + "source": [ + "normalize_score = functools.partial(normalize,\n", + " xmin=data_encoded.Score.min(),\n", + " xmax=data_encoded.Score.max())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nhR0rwUIctTa" + }, + "source": [ + "O valor abaixo realmente confere com aquele encontrado pelo `MinMaxScaler`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "pMfk3jrU1mQV", + "outputId": "f9851c0d-9446-4f10-874e-cdba22b43722" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.485549" + ] + }, + "execution_count": 30, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "normalize_score(data_encoded.Score[0]).round(6)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HEcSQzWJ2Yum" + }, + "source": [ + "## Padronização (_Standardization_)\n", + "\n", + "Padronização é o processo de tornar a variável com média zero e variância um. Esse processo não deve ser confundido com a normalização descrita acima.\n", + "\n", + "O processo é simples, basta subtrair a média dos dados de cada observação e dividi-los pelo desvio-padrão:\n", + "\n", + "$$x_{\\text{standardized}} = \\frac{x - \\bar{x}}{s}$$\n", + "\n", + "onde $\\bar{x}$ indica a média amostral e $s$ o desvio-padrão amostral." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kXYXezCNdYue" + }, + "source": [ + "No exemplo abaixo, padronizamos a variável `Score`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "Qfhs3Eaq2dGV", + "outputId": "572aae65-5460-44d1-8134-dbc26f82e2d2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.20752554],\n", + " [-0.40839081],\n", + " [ 2.26628886],\n", + " [-1.76158843],\n", + " [-0.39781896],\n", + " [ 1.24081879],\n", + " [ 1.15624393],\n", + " [-0.0489477 ],\n", + " [ 0.66993854],\n", + " [-0.84183693]])" + ] + }, + "execution_count": 31, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "standard_scaler = StandardScaler()\n", + "\n", + "standard_scaler.fit(data_encoded[[\"Score\"]])\n", + "\n", + "score_standardized = standard_scaler.transform(data_encoded[[\"Score\"]])\n", + "\n", + "score_standardized[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "SJJucIQddgME" + }, + "source": [ + "E adicionamos a variável padronizada ao nosso _data set_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "BAndWLe13RSr", + "outputId": "4a6231c1-f459-4307-ad14-24c4e46760cd" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourseBiologyMathPhysicsTallScore_interval4.09 ⊢ 5.395.39 ⊢ 6.686.68 ⊢ 7.987.98 ⊢ 9.28Score_normalizedScore_standardized
01.5396.61Biology100False6.20 ⊢ 6.7401000.485549-0.207526
11.7646.42Biology100False6.20 ⊢ 6.7401000.448940-0.408391
21.6958.95Biology100False7.60 ⊢ 9.2800010.9364162.266289
31.8295.14Biology100True4.09 ⊢ 6.2010000.202312-1.761588
41.6406.43Physics001False6.20 ⊢ 6.7401000.450867-0.397819
51.7787.98Physics001False7.60 ⊢ 9.2800100.7495181.240819
61.6797.90Biology100False7.60 ⊢ 9.2800100.7341041.156244
71.6046.76Physics001False6.74 ⊢ 7.6000100.514451-0.048948
81.8197.44Physics001True6.74 ⊢ 7.6000100.6454720.669939
91.6076.01Physics001False4.09 ⊢ 6.2001000.369942-0.841837
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" ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0 False\n", - "1 False\n", - "2 False\n", - "3 True\n", - "4 False\n", - "5 False\n", - "6 False\n", - "7 False\n", - "8 True\n", - "9 False\n", - "Name: Height, dtype: bool" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 11 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "94vcsMVguGvG", - "colab_type": "code", - "outputId": "b2b15447-7399-4309-b18a-3de5a183a41e", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - } - }, - "source": [ - "binarizer = Binarizer(threshold=1.80).fit(data_encoded[[\"Height\"]])\n", - "\n", - "height_binary = binarizer.transform(data_encoded[[\"Height\"]])\n", - "\n", - "height_binary[:10]" + "text/plain": [ + " Height Score Course ... 7.98 ⊢ 9.28 Score_normalized Score_standardized\n", + "0 1.539 6.61 Biology ... 0 0.485549 -0.207526\n", + "1 1.764 6.42 Biology ... 0 0.448940 -0.408391\n", + "2 1.695 8.95 Biology ... 1 0.936416 2.266289\n", + "3 1.829 5.14 Biology ... 0 0.202312 -1.761588\n", + "4 1.640 6.43 Physics ... 0 0.450867 -0.397819\n", + "5 1.778 7.98 Physics ... 0 0.749518 1.240819\n", + "6 1.679 7.90 Biology ... 0 0.734104 1.156244\n", + "7 1.604 6.76 Physics ... 0 0.514451 -0.048948\n", + "8 1.819 7.44 Physics ... 0 0.645472 0.669939\n", + "9 1.607 6.01 Physics ... 0 0.369942 -0.841837\n", + "\n", + "[10 rows x 14 columns]" + ] + }, + "execution_count": 32, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_encoded = pd.concat([data_encoded, pd.DataFrame(score_standardized.flatten(), columns=[\"Score_standardized\"])], axis=1)\n", + "\n", + "data_encoded.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_SgwGLgOdk5Q" + }, + "source": [ + "Note que, ao contrário da variável normalizada, é possível ter valores negativos e positivos, menores e maiores que um. Isso é bem óbvio, pois os dados agora têm média 0 e variância 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "I0E9fwo93h9w", + "outputId": "2d9d5cdf-181b-4ca1-bea7-b382bf738ebd" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.2501111257279262e-15, 1.0101010101010102)" + ] + }, + "execution_count": 33, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_encoded.Score_standardized.mean(), data_encoded.Score_standardized.var()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Av0cwG_Qd3Ow" + }, + "source": [ + "Novamente, para avaliar os resultados obtidos, podemos escrever nossa própria função de padronização:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "khwEkoks3-cS" + }, + "outputs": [], + "source": [ + "def standardize(x, xmean, xstd):\n", + " return (x - xmean)/xstd" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "14w3018J4Gwy" + }, + "outputs": [], + "source": [ + "standardize_score = functools.partial(standardize,\n", + " xmean=data_encoded.Score.mean(),\n", + " xstd=data_encoded.Score.std())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UAGxoUK5d-22" + }, + "source": [ + "Como esperado, o valor confere com o encontrado:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "dpaNVzOy4aCL", + "outputId": "fa0f42f0-32a5-48f4-f8d7-724350cdca86" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.20648530634442175" + ] + }, + "execution_count": 36, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "standardize_score(data_encoded.Score[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2tO4OOJK7NY1" + }, + "source": [ + "## Criando um _Pipeline_\n", + "\n", + "Todo esse processo de transformar os dados pode ser bastante trabalhoso e entendiante. Para facilitar as coisas, o sklearn dispõe de um mecanismo de _pipeline_ que funciona como ao esteira de uma linha de montagem. Cada etapa desse _pipeline_ é uma transformação nos dados, de forma que, ao final do _pipeline_, temos os dados totalmente transformados. A vantagem é que agora especificamos todas as etapas, ou transformações, de uma só vez, e podemos reaproveitar esse _pipeline_ no futuro." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "T1LyaI0-B2hV", + "outputId": "011176a0-ec92-4122-9fc4-3b3d0a3118c9" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourse
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" ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[0.],\n", - " [0.],\n", - " [0.],\n", - " [1.],\n", - " [0.],\n", - " [0.],\n", - " [0.],\n", - " [0.],\n", - " [1.],\n", - " [0.]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 12 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oND_xnxRV8wZ", - "colab_type": "text" - }, - "source": [ - "O `Binarizer` tem como saída uma matriz binária numérica. Podemos transformá-la em um vetor de _bool_:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "iXbf50-4vdDR", - "colab_type": "code", - "outputId": "2f7dba40-f513-491a-e072-743ac0a8c88f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "source": [ - "height_bool = pd.DataFrame(height_binary.flatten().astype(bool), columns=[\"Tall\"])\n", - "\n", - "height_bool.head(10)" + "text/plain": [ + " Height Score Course\n", + "0 1.539 6.61 Biology\n", + "1 1.764 6.42 Biology\n", + "2 1.695 8.95 Biology\n", + "3 1.829 5.14 Biology\n", + "4 1.640 6.43 Physics\n", + "5 1.778 7.98 Physics\n", + "6 1.679 7.90 Biology\n", + "7 1.604 6.76 Physics\n", + "8 1.819 7.44 Physics\n", + "9 1.607 6.01 Physics" + ] + }, + "execution_count": 37, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "86on9pLMeidf" + }, + "source": [ + "Para evitar bagunçar com nosso _data set_ original, criamos uma cópia (rasa) dele:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "vdA8euCcZeq1" + }, + "outputs": [], + "source": [ + "data_missing = data.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "snDUyWqEenh8" + }, + "source": [ + "E para tornar o exemplo mais interessante, adicionamos (ou removemos?) dados faltantes ao _data set_. Isso porque uma das transformações úteis que podemos aplicar no _pipeline_ é justamente a imputação de dados, ou seja, preencher dados faltantes.\n", + "\n", + "As variáveis numéricas faltantes são representadas por `np.nan`, enquanto a variável categórica é representada pela classe `Unknown`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "LkVnbFAKS_fF", + "outputId": "6ba74eb6-0d60-419a-c39a-dd165cd49b60" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightScoreCourse
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8NaN7.44Physics
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15NaN5.44Biology
24NaN8.08Biology
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" ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Tall\n", - "0 False\n", - "1 False\n", - "2 False\n", - "3 True\n", - "4 False\n", - "5 False\n", - "6 False\n", - "7 False\n", - "8 True\n", - "9 False" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 13 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nn9Gs9DhWNvi", - "colab_type": "text" - }, - "source": [ - "Vamos adicionar a nova variável `Tall`, que indica se a pessoa é alta (> 1.80 m), ao nosso _data set_:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "xjOV0WlJy7DY", - "colab_type": "code", - "outputId": "af316c4b-4931-44cb-a4af-4fa51b3c93fc", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "source": [ - "data_encoded = pd.concat([data_encoded, height_bool], axis=1)\n", - "\n", - "data_encoded.head(10)" + "text/plain": [ + " Height Score Course\n", + "2 1.695 NaN Unknown\n", + "5 1.778 NaN Physics\n", + "8 NaN 7.44 Physics\n", + "11 1.539 NaN Biology\n", + "15 NaN 5.44 Biology\n", + "24 NaN 8.08 Biology\n", + "29 2.020 6.83 Unknown\n", + "33 1.691 NaN Math\n", + "35 2.085 6.96 Unknown\n", + "38 1.376 6.54 Unknown" + ] + }, + "execution_count": 39, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "unknown_height_idx = pd.Index(np.random.choice(data_missing.index, 10, replace=False))\n", + "unknown_score_idx = pd.Index(np.random.choice(data_missing.index, 10, replace=False))\n", + "unknown_course_idx = pd.Index(np.random.choice(data_missing.index, 10, replace=False))\n", + "\n", + "data_missing.loc[unknown_height_idx, \"Height\"] = np.nan\n", + "data_missing.loc[unknown_score_idx, \"Score\"] = np.nan\n", + "data_missing.loc[unknown_course_idx, \"Course\"] = \"Unknown\"\n", + "\n", + "data_missing_idx = unknown_height_idx | unknown_score_idx | unknown_course_idx\n", + "\n", + "data_missing.loc[data_missing_idx].head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nmUJS9SzfC9Y" + }, + "source": [ + "Criamos o _pipeline_ com as seguintes etapas:\n", + "\n", + "1. Faça imputação dos dados, preenchendo os dados faltantes com a mediana dos dados presentes.\n", + "2. Faça a normalização dos dados no intervalo _default_ $[0, 1]$.\n", + "3. Crie novas variáveis através da expansão polinomial da variável original." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "9ypslSlEhGBr" + }, + "source": [ + "O `Pipeline` recebe uma lista de transformações representadas por tuplas de dois elementos. Cada tupla contém:\n", + "\n", + "* O nome para a etapa (ou transformação ou estimador). Isso vai ser útil para recuperar algumas informações do _pipeline_ mais a frente.\n", + "* Um objeto da classe do transformador ou estimador, já com seus parâmetros configurados." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "XqthBhA18ITd" + }, + "outputs": [], + "source": [ + "num_pipeline = Pipeline(steps=[\n", + " (\"imputer\", SimpleImputer(strategy=\"median\")),\n", + " (\"minmax_scaler\", MinMaxScaler()),\n", + " (\"poly_features\", PolynomialFeatures(degree=2, include_bias=False))\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3UVr1XWCfZID" + }, + "source": [ + "Depois da especificação do nosso _pipeline_, podemos aplicá-lo simultaneamente a diversas variáveis (desde que as transformações especificadas façam sentido).\n", + "\n", + "No exemplo abaixo, aplicamos esse _pipeline_ às variáveis `Height` e `Score` ao mesmo tempo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "Qh8kbymmDZqB", + "outputId": "0595019a-1288-4ea8-d18b-1d61dc44136b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.26553106, 0.48554913, 0.07050674, 0.12892838, 0.23575796],\n", + " [0.49098196, 0.44894027, 0.24106329, 0.22042158, 0.20154737],\n", + " [0.42184369, 0.48843931, 0.1779521 , 0.20604504, 0.23857296],\n", + " [0.55611222, 0.20231214, 0.30926081, 0.11250825, 0.0409302 ],\n", + " [0.36673347, 0.45086705, 0.13449344, 0.16534804, 0.2032811 ],\n", + " [0.50501002, 0.48843931, 0.25503512, 0.24666674, 0.23857296],\n", + " [0.40581162, 0.73410405, 0.16468307, 0.29790795, 0.53890875],\n", + " [0.33066132, 0.51445087, 0.10933691, 0.170109 , 0.26465969],\n", + " [0.41082164, 0.64547206, 0.16877442, 0.26517389, 0.41663418],\n", + " [0.33366733, 0.3699422 , 0.11133389, 0.12343763, 0.13685723]])" + ] + }, + "execution_count": 41, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline_transformation = num_pipeline.fit_transform(data_missing[[\"Height\", \"Score\"]])\n", + "\n", + "pipeline_transformation[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HoNf9vDJfrW8" + }, + "source": [ + "Para ficar mais claro a saída do _pipeline_, podemos utilizar os nomes das _features_ geradas através do método `get_feature_names()`. Para tornar ainda mais claro, substituímos o que é chamado `x0` por `Height` e `x1` por `Score`, que é inferido pela ordem das variáveis no _pipeline_." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "OJz5zvr2EeM3", + "outputId": "444fe35c-4e5e-4f9c-ef6a-152dd9bcd775" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Height_n', 'Score_n', 'Height_n^2', 'Height_n Score_n', 'Score_n^2']" + ] + }, + "execution_count": 42, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "poly_features = num_pipeline.get_params()[\"poly_features\"].get_feature_names()\n", + " \n", + "pipeline_columns = [old_name.replace(\"x0\", \"Height_n\").replace(\"x1\", \"Score_n\") for old_name in poly_features]\n", + "\n", + "pipeline_columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MBgEafF-gKA3" + }, + "source": [ + "Criamos um novo _data set_ com essas variáveis resultantes do _pipeline_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 237 + }, + "colab_type": "code", + "id": "q_xBepJGIAJm", + "outputId": "6126947b-ef3f-42db-84aa-4317ed5f79d3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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71.6046.76Physics001False
81.8197.44Physics001True
91.6076.01Physics001False
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" - ], - "text/plain": [ - " Height Score Course Biology Math Physics Tall\n", - "0 1.539 6.61 Biology 1 0 0 False\n", - "1 1.764 6.42 Biology 1 0 0 False\n", - "2 1.695 8.95 Biology 1 0 0 False\n", - "3 1.829 5.14 Biology 1 0 0 True\n", - "4 1.640 6.43 Physics 0 0 1 False\n", - "5 1.778 7.98 Physics 0 0 1 False\n", - "6 1.679 7.90 Biology 1 0 0 False\n", - "7 1.604 6.76 Physics 0 0 1 False\n", - "8 1.819 7.44 Physics 0 0 1 True\n", - "9 1.607 6.01 Physics 0 0 1 False" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 14 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2tOdmnNi23p4", - "colab_type": "text" - }, - "source": [ - "## Discretização (_Binning_)\n", - "\n", - "Discretização, como o nome diz, é o processo de discretizar ou separar em intervalos contínuos uma variável numérica. Isso pode ser útil para converter uma variável numérica em categórica, quando o valor exato numérico não for tão importante quanto o intervalo onde ele se encontra.\n", - "\n", - "Podemos criar _bins_ (_buckets_ ou intervalos) que contenham aproximadamente a mesma quantidade de observações, utilizando a estratégia `quantile` ou que sejam igualmente espaçados com a estratégia `uniform`.\n", - "\n", - "No exemplo a seguir, criamos quatro intervalos da variável `Score` com a estratégia `quantile`:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Xir4K6i522ZQ", - "colab_type": "code", - "outputId": "e902850a-d3dc-4d97-a80f-ad3dad1bb1a2", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - } - }, - "source": [ - "discretizer = KBinsDiscretizer(n_bins=4, encode=\"ordinal\", strategy=\"quantile\")\n", - "\n", - "discretizer.fit(data_encoded[[\"Score\"]])\n", - "\n", - "score_bins = discretizer.transform(data_encoded[[\"Score\"]])\n", - "\n", - "score_bins[:10]" + "text/plain": [ + " Height_n Score_n Height_n^2 Height_n Score_n Score_n^2\n", + "0 0.265531 0.485549 0.070507 0.128928 0.235758\n", + "1 0.490982 0.448940 0.241063 0.220422 0.201547\n", + "2 0.421844 0.488439 0.177952 0.206045 0.238573\n", + "3 0.556112 0.202312 0.309261 0.112508 0.040930\n", + "4 0.366733 0.450867 0.134493 0.165348 0.203281\n", + "5 0.505010 0.488439 0.255035 0.246667 0.238573" + ] + }, + "execution_count": 43, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "height_score_normalized_poly = pd.DataFrame(pipeline_transformation, columns=pipeline_columns)\n", + "\n", + "height_score_normalized_poly.head(6)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "9imGtnaygRiX" + }, + "source": [ + "Podemos também criar outro _pipeline_ para a variável categórica `Course`. Como se trata de uma variável de natureza completamente diferente, precisamos especificar um _pipeline_ diferente com as seguintes transformações:\n", + "\n", + "1. Preencha os dados faltantes (`None`) com a classe `Unknown`.\n", + "2. Crie novas variáveis binárias com o `OneHotEncoder`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eZP_HTkchI5c" + }, + "source": [ + "Assim como no _pipeline_ anterior, especificamos cada etapa como uma tupla com um nome e um objeto de um transformador ou estimador:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "NMv_2lV7KxTM" + }, + "outputs": [], + "source": [ + "cat_pipeline = Pipeline([\n", + " (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=\"Unknown\")),\n", + " (\"one_hot_encoder\", OneHotEncoder(sparse=False, dtype=np.int))\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wK66jYTShV52" + }, + "source": [ + "Após a especificação do _pipeline_, podemos aplicá-lo à nossa variável `Course`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KIFWvPS7LNUA" + }, + "outputs": [], + "source": [ + "course_pipeline_transformation = cat_pipeline.fit_transform(data_missing[[\"Course\"]])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "quJ4ThBBhfBI" + }, + "source": [ + "Agora, utilizaremos o nome que demos à etapa do `OneHotEncoder` para recuperar esse transformador através do método `get_params()`. Depois de recuperado o `OneHotEncoder`, acessamos seu atributo `categories_` (primeiro índice `[0]`, pois poderíamos ter aplicado o _pipeline_ a mais de uma variável categórica):" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "Zurb-NVWM4sX", + "outputId": "1e7c2960-6ffb-4285-bb2d-691157302850" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Biology', 'Math', 'Physics', 'Unknown'], dtype=object)" + ] + }, + "execution_count": 46, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "course_columns = cat_pipeline.get_params()[\"one_hot_encoder\"].categories_[0]\n", + "\n", + "course_columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ABQDGjU_iDGS" + }, + "source": [ + "Utilizamos a saída do _pipeline_ e os nomes das categorias recuperados do transformador para criar um novo `DataFrame`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "colab_type": "code", + "id": "3ec56uIcMvll", + "outputId": "5707acac-8d67-4d74-eb02-d73b98f6340a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[1.],\n", - " [1.],\n", - " [3.],\n", - " [0.],\n", - " [1.],\n", - " [3.],\n", - " [3.],\n", - " [2.],\n", - " [2.],\n", - " [0.]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 15 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3hrP6E4xYXCs", - "colab_type": "text" - }, - "source": [ - "Os limites dos intervalos estão disponíveis no atributo `bin_edges_`. Isso pode ser útil para criarmos _labels_ para colunas do _data set_ por exemplo:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "ScCmeNtn3-fF", - "colab_type": "code", - "outputId": "be1003a5-2d28-42d6-e76d-bc349e957e95", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "discretizer.bin_edges_" + "text/plain": [ + " Biology Math Physics Unknown\n", + "0 1 0 0 0\n", + "1 1 0 0 0\n", + "2 0 0 0 1\n", + "3 1 0 0 0\n", + "4 0 0 1 0" + ] + }, + "execution_count": 47, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "course_discretized = pd.DataFrame(course_pipeline_transformation, columns=course_columns)\n", + "\n", + "course_discretized.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OeO6hmSEiL6N" + }, + "source": [ + "Por fim, combinamos as saídas dos dois _pipelines_ para criar um único `DataFrame`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "colab_type": "code", + "id": "d8tL_jS1NTf7", + "outputId": "8b39c1c3-e549-4cea-fade-7c8e90d290ba" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Height_nScore_nHeight_n^2Height_n Score_nScore_n^2BiologyMathPhysicsUnknown
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10.4909820.4489400.2410630.2204220.2015471000
20.4218440.4884390.1779520.2060450.2385730001
30.5561120.2023120.3092610.1125080.0409301000
40.3667330.4508670.1344930.1653480.2032810010
50.5050100.4884390.2550350.2466670.2385730010
60.4058120.7341040.1646830.2979080.5389091000
70.3306610.5144510.1093370.1701090.2646600010
80.4108220.6454720.1687740.2651740.4166340010
90.3336670.3699420.1113340.1234380.1368570010
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" ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([array([4.09 , 6.1975, 6.735 , 7.6 , 9.28 ])], dtype=object)" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 16 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vGl5ONq2Yk7r", - "colab_type": "text" - }, - "source": [ - "A função `get_interval()` abaixo facilita a criação de _labels_ indicativas dos intervalos:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "fvB70_vd4fSO", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def get_interval(bin_idx, bin_edges):\n", - " return f\"{np.round(bin_edges[bin_idx], 2):.2f} ⊢ {np.round(bin_edges[bin_idx+1], 2):.2f}\"" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Hn3eqHFbYtfm", - "colab_type": "text" - }, - "source": [ - "Cada um dos intervalos mostrados abaixo deve possuir aproximadamente a mesma quantidade de observações:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "HX59pepN5ZQQ", - "colab_type": "code", - "outputId": "d5b3d4dc-c969-44cb-fa34-e31fad2dd818", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 139 - } - }, - "source": [ - "bin_edges_quantile = discretizer.bin_edges_[0]\n", - "\n", - "print(f\"Bins quantile\")\n", - "print(f\"interval: #elements\\n\")\n", - "for i in range(len(discretizer.bin_edges_[0])-1):\n", - " print(f\"{get_interval(i, bin_edges_quantile)}: {sum(score_bins[:, 0] == i)}\")" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Bins quantile\n", - "interval: #elements\n", - "\n", - "4.09 ⊢ 6.20: 25\n", - "6.20 ⊢ 6.74: 25\n", - "6.74 ⊢ 7.60: 25\n", - "7.60 ⊢ 9.28: 25\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OQ0fli3IY2G6", - "colab_type": "text" - }, - "source": [ - "A _Series_ abaixo mostra alguns dos intervalos para os quais as observações foram encaixadas:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "SZMBYjqR5-H6", - "colab_type": "code", - "outputId": "cba541dc-9f9e-48d8-eb87-fa54440ca353", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 208 - } - }, - "source": [ - "score_intervals = pd.Series(score_bins.flatten().astype(np.int)).apply(get_interval, args=(bin_edges_quantile,))\n", - "\n", - "score_intervals.head(10)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0 6.20 ⊢ 6.74\n", - "1 6.20 ⊢ 6.74\n", - "2 7.60 ⊢ 9.28\n", - "3 4.09 ⊢ 6.20\n", - "4 6.20 ⊢ 6.74\n", - "5 7.60 ⊢ 9.28\n", - "6 7.60 ⊢ 9.28\n", - "7 6.74 ⊢ 7.60\n", - "8 6.74 ⊢ 7.60\n", - "9 4.09 ⊢ 6.20\n", - "dtype: object" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 19 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6gWE7IU6Y_9q", - "colab_type": "text" - }, - "source": [ - "Também podemos criar uma nova variável, `Score_interval`, no nosso _data set_ com os intervalos (que agora são categorias):" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "fomFOQbVA8eS", - "colab_type": "code", - "outputId": "1f065c4f-6da4-43ad-ebb7-b58706595871", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "source": [ - "data_encoded = pd.concat([data_encoded, pd.DataFrame(score_intervals, columns=[\"Score_interval\"])], axis=1)\n", - "\n", - "data_encoded.head(10)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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HeightScoreCourseBiologyMathPhysicsTallScore_interval
01.5396.61Biology100False6.20 ⊢ 6.74
11.7646.42Biology100False6.20 ⊢ 6.74
21.6958.95Biology100False7.60 ⊢ 9.28
31.8295.14Biology100True4.09 ⊢ 6.20
41.6406.43Physics001False6.20 ⊢ 6.74
51.7787.98Physics001False7.60 ⊢ 9.28
61.6797.90Biology100False7.60 ⊢ 9.28
71.6046.76Physics001False6.74 ⊢ 7.60
81.8197.44Physics001True6.74 ⊢ 7.60
91.6076.01Physics001False4.09 ⊢ 6.20
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" - ], - "text/plain": [ - " Height Score Course Biology Math Physics Tall Score_interval\n", - "0 1.539 6.61 Biology 1 0 0 False 6.20 ⊢ 6.74\n", - "1 1.764 6.42 Biology 1 0 0 False 6.20 ⊢ 6.74\n", - "2 1.695 8.95 Biology 1 0 0 False 7.60 ⊢ 9.28\n", - "3 1.829 5.14 Biology 1 0 0 True 4.09 ⊢ 6.20\n", - "4 1.640 6.43 Physics 0 0 1 False 6.20 ⊢ 6.74\n", - "5 1.778 7.98 Physics 0 0 1 False 7.60 ⊢ 9.28\n", - "6 1.679 7.90 Biology 1 0 0 False 7.60 ⊢ 9.28\n", - "7 1.604 6.76 Physics 0 0 1 False 6.74 ⊢ 7.60\n", - "8 1.819 7.44 Physics 0 0 1 True 6.74 ⊢ 7.60\n", - "9 1.607 6.01 Physics 0 0 1 False 4.09 ⊢ 6.20" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 20 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LldlZ92lZN1k", - "colab_type": "text" - }, - "source": [ - "Como dito, podemos utilizar a estratégia `uniform` para criar _bins_ igualmente espaçados, independente do número de observações que cada um possui. Também podemos especificar o tipo de codificação utilizada. No caso a seguir, utilizamos `encode=onehot-dense` para informar que queremos que a saída seja codificada como o _one-hot encode_ visto anteriormente:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "E6L1qXuW-v-n", - "colab_type": "code", - "outputId": "956f9e9f-67ba-436f-f457-889ee2d1f3db", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - } - }, - "source": [ - "discretizer_uniform = KBinsDiscretizer(n_bins=4, encode=\"onehot-dense\", strategy=\"uniform\")\n", - "\n", - "discretizer_uniform.fit(data_encoded[[\"Score\"]])\n", - "\n", - "score_bins_uniform = discretizer_uniform.transform(data_encoded[[\"Score\"]]).astype(np.int)\n", - "\n", - "score_bins_uniform[:10]" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[0, 1, 0, 0],\n", - " [0, 1, 0, 0],\n", - " [0, 0, 0, 1],\n", - " [1, 0, 0, 0],\n", - " [0, 1, 0, 0],\n", - " [0, 0, 1, 0],\n", - " [0, 0, 1, 0],\n", - " [0, 0, 1, 0],\n", - " [0, 0, 1, 0],\n", - " [0, 1, 0, 0]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 21 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YapI8RuMZZfM", - "colab_type": "text" - }, - "source": [ - "Note como agora os intervalos são ligeiramente diferentes:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "P8gW9k-w-_CC", - "colab_type": "code", - "outputId": "731fca86-f052-4a93-e5bf-e13eec18ac8b", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "bin_edges_uniform = discretizer_uniform.bin_edges_[0]\n", - "\n", - "bin_edges_uniform" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([4.09 , 5.3875, 6.685 , 7.9825, 9.28 ])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 22 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "ieyy46EJAnb6", - "colab_type": "code", - "outputId": "99835fa9-8003-4060-afae-2c4de66685ff", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 139 - } - }, - "source": [ - "score_intervals_columns = [get_interval(i, bin_edges_uniform) for i in range(4)]\n", - "\n", - "print(f\"Bins uniform\")\n", - "print(f\"interval: #elements\\n\")\n", - "for i in range(len(discretizer_uniform.bin_edges_[0])-1):\n", - " print(f\"{get_interval(i, bin_edges_uniform)}: {sum(score_bins_uniform[:, i])}\")" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Bins uniform\n", - "interval: #elements\n", - "\n", - "4.09 ⊢ 5.39: 6\n", - "5.39 ⊢ 6.68: 43\n", - "6.68 ⊢ 7.98: 44\n", - "7.98 ⊢ 9.28: 7\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WuWi-1U4Zzf_", - "colab_type": "text" - }, - "source": [ - "Podemos adicionar as novas variáveis binárias no _data set_:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "P-v3UgiQB87S", - "colab_type": "code", - "outputId": "ad22d68f-c0e8-4a91-8838-842e7e2f5041", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "source": [ - "data_encoded = pd.concat([data_encoded, pd.DataFrame(score_bins_uniform, columns=score_intervals_columns)], axis=1)\n", - "\n", - "data_encoded.head(10)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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HeightScoreCourseBiologyMathPhysicsTallScore_interval4.09 ⊢ 5.395.39 ⊢ 6.686.68 ⊢ 7.987.98 ⊢ 9.28
01.5396.61Biology100False6.20 ⊢ 6.740100
11.7646.42Biology100False6.20 ⊢ 6.740100
21.6958.95Biology100False7.60 ⊢ 9.280001
31.8295.14Biology100True4.09 ⊢ 6.201000
41.6406.43Physics001False6.20 ⊢ 6.740100
51.7787.98Physics001False7.60 ⊢ 9.280010
61.6797.90Biology100False7.60 ⊢ 9.280010
71.6046.76Physics001False6.74 ⊢ 7.600010
81.8197.44Physics001True6.74 ⊢ 7.600010
91.6076.01Physics001False4.09 ⊢ 6.200100
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" - ], - "text/plain": [ - " Height Score Course ... 5.39 ⊢ 6.68 6.68 ⊢ 7.98 7.98 ⊢ 9.28\n", - "0 1.539 6.61 Biology ... 1 0 0\n", - "1 1.764 6.42 Biology ... 1 0 0\n", - "2 1.695 8.95 Biology ... 0 0 1\n", - "3 1.829 5.14 Biology ... 0 0 0\n", - "4 1.640 6.43 Physics ... 1 0 0\n", - "5 1.778 7.98 Physics ... 0 1 0\n", - "6 1.679 7.90 Biology ... 0 1 0\n", - "7 1.604 6.76 Physics ... 0 1 0\n", - "8 1.819 7.44 Physics ... 0 1 0\n", - "9 1.607 6.01 Physics ... 1 0 0\n", - "\n", - "[10 rows x 12 columns]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 24 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jD8WM_-yzqSc", - "colab_type": "text" - }, - "source": [ - "## Normalização (_Scaling_)\n", - "\n", - "Normalização é o processo de colocar uma variável numérica em uma escala pré-determinada, geralmente $[0, 1]$, mas também é comum ser $[-1, 1]$.\n", - "\n", - "Para colocar no intervalo $[0, 1]$, basta subtrair cada valor da valor mínimo e dividir pela diferença do valor máximo e mínimo:\n", - "\n", - "$$x_{\\text{scaled}} = \\frac{x - x_{\\text{min}}}{x_{\\text{max}} - x_{\\text{min}}}$$\n", - "\n", - "Abaixo, escalamos a variável `Score` no intervalo $[0, 1]$:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "nMM2mu-Qzwnv", - "colab_type": "code", - "outputId": "5c60c83b-13bf-431d-e77e-a2fb2e8af317", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - } - }, - "source": [ - "minmax_scaler = MinMaxScaler(feature_range=(0, 1)) # Default feature_scale é (0, 1).\n", - "\n", - "minmax_scaler.fit(data_encoded[[\"Score\"]])\n", - "\n", - "score_normalized = minmax_scaler.transform(data_encoded[[\"Score\"]])\n", - "\n", - "score_normalized[:10]" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[0.48554913],\n", - " [0.44894027],\n", - " [0.93641618],\n", - " [0.20231214],\n", - " [0.45086705],\n", - " [0.7495183 ],\n", - " [0.73410405],\n", - " [0.51445087],\n", - " [0.64547206],\n", - " [0.3699422 ]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 25 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "FPr-37M2UBj4", - "colab_type": "code", - "outputId": "dc170301-56af-4cab-da7c-307c5cbb94a6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "score_normalized.min(), score_normalized.max()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(0.0, 0.9999999999999999)" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 26 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Et6m_2Bbbq-n", - "colab_type": "text" - }, - "source": [ - "Adicionamos a variável `Score` normalizada ao nosso _data set_:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "kaYvCQtK0fzi", - "colab_type": "code", - "outputId": "9f8ccb6c-d0b7-4445-96c9-490f284f2357", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "source": [ - "data_encoded = pd.concat([data_encoded, pd.DataFrame(score_normalized.flatten(), columns=[\"Score_normalized\"])], axis=1)\n", - "\n", - "data_encoded.head(10)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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HeightScoreCourseBiologyMathPhysicsTallScore_interval4.09 ⊢ 5.395.39 ⊢ 6.686.68 ⊢ 7.987.98 ⊢ 9.28Score_normalized
01.5396.61Biology100False6.20 ⊢ 6.7401000.485549
11.7646.42Biology100False6.20 ⊢ 6.7401000.448940
21.6958.95Biology100False7.60 ⊢ 9.2800010.936416
31.8295.14Biology100True4.09 ⊢ 6.2010000.202312
41.6406.43Physics001False6.20 ⊢ 6.7401000.450867
51.7787.98Physics001False7.60 ⊢ 9.2800100.749518
61.6797.90Biology100False7.60 ⊢ 9.2800100.734104
71.6046.76Physics001False6.74 ⊢ 7.6000100.514451
81.8197.44Physics001True6.74 ⊢ 7.6000100.645472
91.6076.01Physics001False4.09 ⊢ 6.2001000.369942
\n", - "
" - ], - "text/plain": [ - " Height Score Course ... 6.68 ⊢ 7.98 7.98 ⊢ 9.28 Score_normalized\n", - "0 1.539 6.61 Biology ... 0 0 0.485549\n", - "1 1.764 6.42 Biology ... 0 0 0.448940\n", - "2 1.695 8.95 Biology ... 0 1 0.936416\n", - "3 1.829 5.14 Biology ... 0 0 0.202312\n", - "4 1.640 6.43 Physics ... 0 0 0.450867\n", - "5 1.778 7.98 Physics ... 1 0 0.749518\n", - "6 1.679 7.90 Biology ... 1 0 0.734104\n", - "7 1.604 6.76 Physics ... 1 0 0.514451\n", - "8 1.819 7.44 Physics ... 1 0 0.645472\n", - "9 1.607 6.01 Physics ... 0 0 0.369942\n", - "\n", - "[10 rows x 13 columns]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 27 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "n7-msElsbveR", - "colab_type": "text" - }, - "source": [ - "Para avaliar se os valores encontrados conferem, podemos utilizar a função `normalize` abaixo:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "EAfUGaFc061d", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def normalize(x, xmin, xmax):\n", - " return (x - xmin)/(xmax - xmin)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CXywxNX-b-0K", - "colab_type": "text" - }, - "source": [ - "A função `partial()` do módulo `functools` (_builtin_ do Python) permite \"congelar\" alguns parâmetros da função passaga como argumento, facilitando a invocação desta função quando tais parâmetros são constantes. No caso abaixo, \"congelamos\" os argumentos `xmin` e `xmax` da função `normalize()` com os valores mínimo e máximo da variável `Score`, respectivamente. Nas invocações subsequentes de `normalize` não precisaremos passar esses argumentos, somente o argumento \"não congelado\" `x`:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "UAlpigp21OVx", - "colab_type": "code", - "colab": {} - }, - "source": [ - "normalize_score = functools.partial(normalize,\n", - " xmin=data_encoded.Score.min(),\n", - " xmax=data_encoded.Score.max())" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nhR0rwUIctTa", - "colab_type": "text" - }, - "source": [ - "O valor abaixo realmente confere com aquele encontrado pelo `MinMaxScaler`:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "pMfk3jrU1mQV", - "colab_type": "code", - "outputId": "f9851c0d-9446-4f10-874e-cdba22b43722", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "normalize_score(data_encoded.Score[0]).round(6)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0.485549" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 30 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HEcSQzWJ2Yum", - "colab_type": "text" - }, - "source": [ - "## Padronização (_Standardization_)\n", - "\n", - "Padronização é o processo de tornar a variável com média zero e variância um. Esse processo não deve ser confundido com a normalização descrita acima.\n", - "\n", - "O processo é simples, basta subtrair a média dos dados de cada observação e dividi-los pelo desvio-padrão:\n", - "\n", - "$$x_{\\text{standardized}} = \\frac{x - \\bar{x}}{s}$$\n", - "\n", - "onde $\\bar{x}$ indica a média amostral e $s$ o desvio-padrão amostral." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kXYXezCNdYue", - "colab_type": "text" - }, - "source": [ - "No exemplo abaixo, padronizamos a variável `Score`:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Qfhs3Eaq2dGV", - "colab_type": "code", - "outputId": "572aae65-5460-44d1-8134-dbc26f82e2d2", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - } - }, - "source": [ - "standard_scaler = StandardScaler()\n", - "\n", - "standard_scaler.fit(data_encoded[[\"Score\"]])\n", - "\n", - "score_standardized = standard_scaler.transform(data_encoded[[\"Score\"]])\n", - "\n", - "score_standardized[:10]" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[-0.20752554],\n", - " [-0.40839081],\n", - " [ 2.26628886],\n", - " [-1.76158843],\n", - " [-0.39781896],\n", - " [ 1.24081879],\n", - " [ 1.15624393],\n", - " [-0.0489477 ],\n", - " [ 0.66993854],\n", - " [-0.84183693]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 31 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SJJucIQddgME", - "colab_type": "text" - }, - "source": [ - "E adicionamos a variável padronizada ao nosso _data set_:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "BAndWLe13RSr", - "colab_type": "code", - "outputId": "4a6231c1-f459-4307-ad14-24c4e46760cd", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "source": [ - "data_encoded = pd.concat([data_encoded, pd.DataFrame(score_standardized.flatten(), columns=[\"Score_standardized\"])], axis=1)\n", - "\n", - "data_encoded.head(10)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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HeightScoreCourseBiologyMathPhysicsTallScore_interval4.09 ⊢ 5.395.39 ⊢ 6.686.68 ⊢ 7.987.98 ⊢ 9.28Score_normalizedScore_standardized
01.5396.61Biology100False6.20 ⊢ 6.7401000.485549-0.207526
11.7646.42Biology100False6.20 ⊢ 6.7401000.448940-0.408391
21.6958.95Biology100False7.60 ⊢ 9.2800010.9364162.266289
31.8295.14Biology100True4.09 ⊢ 6.2010000.202312-1.761588
41.6406.43Physics001False6.20 ⊢ 6.7401000.450867-0.397819
51.7787.98Physics001False7.60 ⊢ 9.2800100.7495181.240819
61.6797.90Biology100False7.60 ⊢ 9.2800100.7341041.156244
71.6046.76Physics001False6.74 ⊢ 7.6000100.514451-0.048948
81.8197.44Physics001True6.74 ⊢ 7.6000100.6454720.669939
91.6076.01Physics001False4.09 ⊢ 6.2001000.369942-0.841837
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" - ], - "text/plain": [ - " Height Score Course ... 7.98 ⊢ 9.28 Score_normalized Score_standardized\n", - "0 1.539 6.61 Biology ... 0 0.485549 -0.207526\n", - "1 1.764 6.42 Biology ... 0 0.448940 -0.408391\n", - "2 1.695 8.95 Biology ... 1 0.936416 2.266289\n", - "3 1.829 5.14 Biology ... 0 0.202312 -1.761588\n", - "4 1.640 6.43 Physics ... 0 0.450867 -0.397819\n", - "5 1.778 7.98 Physics ... 0 0.749518 1.240819\n", - "6 1.679 7.90 Biology ... 0 0.734104 1.156244\n", - "7 1.604 6.76 Physics ... 0 0.514451 -0.048948\n", - "8 1.819 7.44 Physics ... 0 0.645472 0.669939\n", - "9 1.607 6.01 Physics ... 0 0.369942 -0.841837\n", - "\n", - "[10 rows x 14 columns]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 32 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_SgwGLgOdk5Q", - "colab_type": "text" - }, - "source": [ - "Note que, ao contrário da variável normalizada, é possível ter valores negativos e positivos, menores e maiores que um. Isso é bem óbvio, pois os dados agora têm média 0 e variância 1:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "I0E9fwo93h9w", - "colab_type": "code", - "outputId": "2d9d5cdf-181b-4ca1-bea7-b382bf738ebd", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "data_encoded.Score_standardized.mean(), data_encoded.Score_standardized.var()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(-1.2501111257279262e-15, 1.0101010101010102)" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 33 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Av0cwG_Qd3Ow", - "colab_type": "text" - }, - "source": [ - "Novamente, para avaliar os resultados obtidos, podemos escrever nossa própria função de padronização:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "khwEkoks3-cS", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def standardize(x, xmean, xstd):\n", - " return (x - xmean)/xstd" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "14w3018J4Gwy", - "colab_type": "code", - "colab": {} - }, - "source": [ - "standardize_score = functools.partial(standardize,\n", - " xmean=data_encoded.Score.mean(),\n", - " xstd=data_encoded.Score.std())" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UAGxoUK5d-22", - "colab_type": "text" - }, - "source": [ - "Como esperado, o valor confere com o encontrado:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "dpaNVzOy4aCL", - "colab_type": "code", - "outputId": "fa0f42f0-32a5-48f4-f8d7-724350cdca86", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "standardize_score(data_encoded.Score[0])" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "-0.20648530634442175" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 36 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2tO4OOJK7NY1", - "colab_type": "text" - }, - "source": [ - "## Criando um _Pipeline_\n", - "\n", - "Todo esse processo de transformar os dados pode ser bastante trabalhoso e entendiante. Para facilitar as coisas, o sklearn dispõe de um mecanismo de _pipeline_ que funciona como ao esteira de uma linha de montagem. Cada etapa desse _pipeline_ é uma transformação nos dados, de forma que, ao final do _pipeline_, temos os dados totalmente transformados. A vantagem é que agora especificamos todas as etapas, ou transformações, de uma só vez, e podemos reaproveitar esse _pipeline_ no futuro." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "T1LyaI0-B2hV", - "colab_type": "code", - "outputId": "011176a0-ec92-4122-9fc4-3b3d0a3118c9", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "source": [ - "data.head(10)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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HeightScoreCourse
01.5396.61Biology
11.7646.42Biology
21.6958.95Biology
31.8295.14Biology
41.6406.43Physics
51.7787.98Physics
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71.6046.76Physics
81.8197.44Physics
91.6076.01Physics
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" - ], - "text/plain": [ - " Height Score Course\n", - "0 1.539 6.61 Biology\n", - "1 1.764 6.42 Biology\n", - "2 1.695 8.95 Biology\n", - "3 1.829 5.14 Biology\n", - "4 1.640 6.43 Physics\n", - "5 1.778 7.98 Physics\n", - "6 1.679 7.90 Biology\n", - "7 1.604 6.76 Physics\n", - "8 1.819 7.44 Physics\n", - "9 1.607 6.01 Physics" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 37 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "86on9pLMeidf", - "colab_type": "text" - }, - "source": [ - "Para evitar bagunçar com nosso _data set_ original, criamos uma cópia (rasa) dele:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "vdA8euCcZeq1", - "colab_type": "code", - "colab": {} - }, - "source": [ - "data_missing = data.copy()" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "snDUyWqEenh8", - "colab_type": "text" - }, - "source": [ - "E para tornar o exemplo mais interessante, adicionamos (ou removemos?) dados faltantes ao _data set_. Isso porque uma das transformações úteis que podemos aplicar no _pipeline_ é justamente a imputação de dados, ou seja, preencher dados faltantes.\n", - "\n", - "As variáveis numéricas faltantes são representadas por `np.nan`, enquanto a variável categórica é representada pela classe `Unknown`:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "LkVnbFAKS_fF", - "colab_type": "code", - "outputId": "6ba74eb6-0d60-419a-c39a-dd165cd49b60", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "source": [ - "unknown_height_idx = pd.Index(np.random.choice(data_missing.index, 10, replace=False))\n", - "unknown_score_idx = pd.Index(np.random.choice(data_missing.index, 10, replace=False))\n", - "unknown_course_idx = pd.Index(np.random.choice(data_missing.index, 10, replace=False))\n", - "\n", - "data_missing.loc[unknown_height_idx, \"Height\"] = np.nan\n", - "data_missing.loc[unknown_score_idx, \"Score\"] = np.nan\n", - "data_missing.loc[unknown_course_idx, \"Course\"] = \"Unknown\"\n", - "\n", - "data_missing_idx = unknown_height_idx | unknown_score_idx | unknown_course_idx\n", - "\n", - "data_missing.loc[data_missing_idx].head(10)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
HeightScoreCourse
21.695NaNUnknown
51.778NaNPhysics
8NaN7.44Physics
111.539NaNBiology
15NaN5.44Biology
24NaN8.08Biology
292.0206.83Unknown
331.691NaNMath
352.0856.96Unknown
381.3766.54Unknown
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" - ], - "text/plain": [ - " Height Score Course\n", - "2 1.695 NaN Unknown\n", - "5 1.778 NaN Physics\n", - "8 NaN 7.44 Physics\n", - "11 1.539 NaN Biology\n", - "15 NaN 5.44 Biology\n", - "24 NaN 8.08 Biology\n", - "29 2.020 6.83 Unknown\n", - "33 1.691 NaN Math\n", - "35 2.085 6.96 Unknown\n", - "38 1.376 6.54 Unknown" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 39 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nmUJS9SzfC9Y", - "colab_type": "text" - }, - "source": [ - "Criamos o _pipeline_ com as seguintes etapas:\n", - "\n", - "1. Faça imputação dos dados, preenchendo os dados faltantes com a mediana dos dados presentes.\n", - "2. Faça a normalização dos dados no intervalo _default_ $[0, 1]$.\n", - "3. Crie novas variáveis através da expansão polinomial da variável original." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9ypslSlEhGBr", - "colab_type": "text" - }, - "source": [ - "O `Pipeline` recebe uma lista de transformações representadas por tuplas de dois elementos. Cada tupla contém:\n", - "\n", - "* O nome para a etapa (ou transformação ou estimador). Isso vai ser útil para recuperar algumas informações do _pipeline_ mais a frente.\n", - "* Um objeto da classe do transformador ou estimador, já com seus parâmetros configurados." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "XqthBhA18ITd", - "colab_type": "code", - "colab": {} - }, - "source": [ - "num_pipeline = Pipeline(steps=[\n", - " (\"imputer\", SimpleImputer(strategy=\"median\")),\n", - " (\"minmax_scaler\", MinMaxScaler()),\n", - " (\"poly_features\", PolynomialFeatures(degree=2, include_bias=False))\n", - "])" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3UVr1XWCfZID", - "colab_type": "text" - }, - "source": [ - "Depois da especificação do nosso _pipeline_, podemos aplicá-lo simultaneamente a diversas variáveis (desde que as transformações especificadas façam sentido).\n", - "\n", - "No exemplo abaixo, aplicamos esse _pipeline_ às variáveis `Height` e `Score` ao mesmo tempo:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Qh8kbymmDZqB", - "colab_type": "code", - "outputId": "0595019a-1288-4ea8-d18b-1d61dc44136b", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 191 - } - }, - "source": [ - "pipeline_transformation = num_pipeline.fit_transform(data_missing[[\"Height\", \"Score\"]])\n", - "\n", - "pipeline_transformation[:10]" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[0.26553106, 0.48554913, 0.07050674, 0.12892838, 0.23575796],\n", - " [0.49098196, 0.44894027, 0.24106329, 0.22042158, 0.20154737],\n", - " [0.42184369, 0.48843931, 0.1779521 , 0.20604504, 0.23857296],\n", - " [0.55611222, 0.20231214, 0.30926081, 0.11250825, 0.0409302 ],\n", - " [0.36673347, 0.45086705, 0.13449344, 0.16534804, 0.2032811 ],\n", - " [0.50501002, 0.48843931, 0.25503512, 0.24666674, 0.23857296],\n", - " [0.40581162, 0.73410405, 0.16468307, 0.29790795, 0.53890875],\n", - " [0.33066132, 0.51445087, 0.10933691, 0.170109 , 0.26465969],\n", - " [0.41082164, 0.64547206, 0.16877442, 0.26517389, 0.41663418],\n", - " [0.33366733, 0.3699422 , 0.11133389, 0.12343763, 0.13685723]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 41 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HoNf9vDJfrW8", - "colab_type": "text" - }, - "source": [ - "Para ficar mais claro a saída do _pipeline_, podemos utilizar os nomes das _features_ geradas através do método `get_feature_names()`. Para tornar ainda mais claro, substituímos o que é chamado `x0` por `Height` e `x1` por `Score`, que é inferido pela ordem das variáveis no _pipeline_." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "OJz5zvr2EeM3", - "colab_type": "code", - "outputId": "444fe35c-4e5e-4f9c-ef6a-152dd9bcd775", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "poly_features = num_pipeline.get_params()[\"poly_features\"].get_feature_names()\n", - " \n", - "pipeline_columns = [old_name.replace(\"x0\", \"Height_n\").replace(\"x1\", \"Score_n\") for old_name in poly_features]\n", - "\n", - "pipeline_columns" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "['Height_n', 'Score_n', 'Height_n^2', 'Height_n Score_n', 'Score_n^2']" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 42 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MBgEafF-gKA3", - "colab_type": "text" - }, - "source": [ - "Criamos um novo _data set_ com essas variáveis resultantes do _pipeline_:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "q_xBepJGIAJm", - "colab_type": "code", - "outputId": "6126947b-ef3f-42db-84aa-4317ed5f79d3", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 237 - } - }, - "source": [ - "height_score_normalized_poly = pd.DataFrame(pipeline_transformation, columns=pipeline_columns)\n", - "\n", - "height_score_normalized_poly.head(6)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Height_n Score_n Height_n^2 Height_n Score_n Score_n^2\n", - "0 0.265531 0.485549 0.070507 0.128928 0.235758\n", - "1 0.490982 0.448940 0.241063 0.220422 0.201547\n", - "2 0.421844 0.488439 0.177952 0.206045 0.238573\n", - "3 0.556112 0.202312 0.309261 0.112508 0.040930\n", - "4 0.366733 0.450867 0.134493 0.165348 0.203281\n", - "5 0.505010 0.488439 0.255035 0.246667 0.238573" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 43 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9imGtnaygRiX", - "colab_type": "text" - }, - "source": [ - "Podemos também criar outro _pipeline_ para a variável categórica `Course`. Como se trata de uma variável de natureza completamente diferente, precisamos especificar um _pipeline_ diferente com as seguintes transformações:\n", - "\n", - "1. Preencha os dados faltantes (`None`) com a classe `Unknown`.\n", - "2. Crie novas variáveis binárias com o `OneHotEncoder`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "eZP_HTkchI5c", - "colab_type": "text" - }, - "source": [ - "Assim como no _pipeline_ anterior, especificamos cada etapa como uma tupla com um nome e um objeto de um transformador ou estimador:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "NMv_2lV7KxTM", - "colab_type": "code", - "colab": {} - }, - "source": [ - "cat_pipeline = Pipeline([\n", - " (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=\"Unknown\")),\n", - " (\"one_hot_encoder\", OneHotEncoder(sparse=False, dtype=np.int))\n", - "])" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wK66jYTShV52", - "colab_type": "text" - }, - "source": [ - "Após a especificação do _pipeline_, podemos aplicá-lo à nossa variável `Course`:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "KIFWvPS7LNUA", - "colab_type": "code", - "colab": {} - }, - "source": [ - "course_pipeline_transformation = cat_pipeline.fit_transform(data_missing[[\"Course\"]])" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "quJ4ThBBhfBI", - "colab_type": "text" - }, - "source": [ - "Agora, utilizaremos o nome que demos à etapa do `OneHotEncoder` para recuperar esse transformador através do método `get_params()`. Depois de recuperado o `OneHotEncoder`, acessamos seu atributo `categories_` (primeiro índice `[0]`, pois poderíamos ter aplicado o _pipeline_ a mais de uma variável categórica):" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Zurb-NVWM4sX", - "colab_type": "code", - "outputId": "1e7c2960-6ffb-4285-bb2d-691157302850", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "course_columns = cat_pipeline.get_params()[\"one_hot_encoder\"].categories_[0]\n", - "\n", - "course_columns" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array(['Biology', 'Math', 'Physics', 'Unknown'], dtype=object)" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 46 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ABQDGjU_iDGS", - "colab_type": "text" - }, - "source": [ - "Utilizamos a saída do _pipeline_ e os nomes das categorias recuperados do transformador para criar um novo `DataFrame`:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "3ec56uIcMvll", - "colab_type": "code", - "outputId": "5707acac-8d67-4d74-eb02-d73b98f6340a", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - } - }, - "source": [ - "course_discretized = pd.DataFrame(course_pipeline_transformation, columns=course_columns)\n", - "\n", - "course_discretized.head(5)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Biology Math Physics Unknown\n", - "0 1 0 0 0\n", - "1 1 0 0 0\n", - "2 0 0 0 1\n", - "3 1 0 0 0\n", - "4 0 0 1 0" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 47 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OeO6hmSEiL6N", - "colab_type": "text" - }, - "source": [ - "Por fim, combinamos as saídas dos dois _pipelines_ para criar um único `DataFrame`:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "d8tL_jS1NTf7", - "colab_type": "code", - "outputId": "8b39c1c3-e549-4cea-fade-7c8e90d290ba", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "source": [ - "data_transformed = pd.concat([height_score_normalized_poly, course_discretized], axis=1)\n", - "\n", - "data_transformed.head(10)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Height_n Score_n Height_n^2 ... 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Eles também podem (e devem) ser usados para submeter os dados de teste e até de produção aos mesmos procedimentos dos dados de treinamento." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SbShR7kMZGwE", - "colab_type": "text" - }, - "source": [ - "## _Outliers_\n", - "\n", - "_Outliers_, os famosos \"pontos fora da curva\", são observações que não parecem seguir o mesmo padrão dos demais dados. Eles podem vir de distribuições diferentes, serem erros na coleta de dados, erros de medição etc.\n", - "\n", - "Eles influenciam nossas análises e os nossos algoritmos ao apresentar comportamento distoante do resto do _data set_, impactando na média, variância, funções de perda e custo etc. Se fizer sentido, eles devem ser removidos ou transformados antes de prosseguirmos com a análise.\n", - "\n", - "No entanto, devemos julgar com cautela sua remoção: __alguns _outliers_ são dados autênticos e devem ser estudados com atenção__. Por exemplo, a remoção de uma medição muito alta na temperatura de um reator seria um erro, pois essa medição pode estar nos indicando um potencial problema com o dispositivo.\n", - "\n", - "Abaixo estudamos algumas técnicas simples para encontrar _outliers_.\n", - "\n", - "![outlier](https://www.stats4stem.org/common/web/plugins/ckeditor/plugins/doksoft_uploader/userfiles/WithInfOutlier.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "u3bsTDv0pAN4", - "colab_type": "text" - }, - "source": [ - "Começamos criando uma cópia da variável `Height` do nosso _data set_ para não impactar o original:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "tQ7AQztcZkYx", - "colab_type": "code", - "colab": {} - }, - "source": [ - "height_outlier = data.Height.copy()" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VQNHBAu4pHcp", - "colab_type": "text" - }, - "source": [ - "Adicionamos dez _outliers_ que representam pessoas estranhamente baixas ou estranhamente altas para o padrão que estamos observando:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "nX2R3V0HZI0w", - "colab_type": "code", - "outputId": "6acbd63c-820e-485a-cde4-72a69fefe13d", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 208 - } - }, - "source": [ - "height_outlier_idx = pd.Index(np.random.choice(height_outlier.index, 10, replace=False))\n", - "\n", - "too_short_idx = pd.Index(height_outlier_idx[:5])\n", - "too_tall_idx = pd.Index(height_outlier_idx[5:])\n", - "\n", - "height_outlier[too_short_idx] = np.random.normal(loc=1.30, scale=0.5, size=5)\n", - "height_outlier[too_tall_idx] = np.random.normal(loc=2.20, scale=0.5, size=5)\n", - "\n", - "outlier_idx = too_short_idx | too_tall_idx\n", - "\n", - "height_outlier[outlier_idx]\n" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "14 1.646795\n", - "18 1.696510\n", - "29 0.516665\n", - "38 2.943781\n", - "48 1.058498\n", - "49 1.326605\n", - "57 2.074231\n", - "66 1.831315\n", - "68 2.737088\n", - "96 1.966029\n", - "Name: Height, dtype: float64" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 50 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mwNbTzDnpoDL", - "colab_type": "text" - }, - "source": [ - "Note que nem todos dados gerados se tornaram realmente _outliers_. Como geramos de uma distribuição aleatória, corremos esse risco.\n", - "\n", - "No entanto, temos alguns dados estranhos como 0.51 m e 2.73 m." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "x5pwD_1EqRNZ", - "colab_type": "text" - }, - "source": [ - "No _boxplot_ padrão, os dados mais extremos são mostrados como pontos fora do alcance dos _whiskers_ (as barrinhas do _box plot_).\n", - "\n", - "No caso abaixo, notamos três pontos acima e três pontos abaixo do considerado \"dentro da faixa normal\"." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "hRMVhYz3b2KH", - "colab_type": "code", - "outputId": "9e090cef-804c-4f17-958b-5e25154662db", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 695 - } - }, - "source": [ - "sns.boxplot(height_outlier, orient=\"vertical\");" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MOKP49JMqTog", - "colab_type": "text" - }, - "source": [ - "Uma primeira abordagem bem simples é encontrar os pontos do _box plot_ acima.\n", - "\n", - "Tudo que estiver fora da faixa $[Q1 - 1.5 \\times \\text{IQR}, Q3 + 1.5 \\times \\text{IQR}]$ é considerado um ponto anômalo para aquele padrão:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "z_h0zaVDce0N", - "colab_type": "code", - "outputId": "86b9e772-6438-4820-87ba-dab83a4b1dd8", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "q1 = height_outlier.quantile(0.25)\n", - "q3 = height_outlier.quantile(0.75)\n", - "iqr = q3 - q1\n", - "\n", - "non_outlier_interval_iqr = [q1 - 1.5 * iqr, q3 + 1.5 * iqr]\n", - "\n", - "print(f\"Faixa considerada \\\"normal\\\": {non_outlier_interval_iqr}\")" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Faixa considerada \"normal\": [1.18575, 2.24175]\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wsuVvr8hq4Rc", - "colab_type": "text" - }, - "source": [ - "Agora podemos identificar quais pontos encontram-se fora desse intervalo, ou seja, podem ser considerados _outliers_:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "hm78PWbhc9Dz", - "colab_type": "code", - "outputId": "ee3995ea-8a63-4c90-b3dd-57ba673887ee", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 139 - } - }, - "source": [ - "outliers_iqr = height_outlier[(height_outlier < non_outlier_interval_iqr[0]) | (height_outlier > non_outlier_interval_iqr[1])]\n", - "\n", - "outliers_iqr" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "29 0.516665\n", - "38 2.943781\n", - "48 1.058498\n", - "68 2.737088\n", - "91 2.272000\n", - "92 1.164000\n", - "Name: Height, dtype: float64" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 53 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XcF70kmerGEq", - "colab_type": "text" - }, - "source": [ - "Se estivermos seguos de que esses pontos representam de fato _outliers_ e que sua remoção não traz prejuízo à nossa análise, então podemos removê-los:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "BVRJS9DNeb9z", - "colab_type": "code", - "colab": {} - }, - "source": [ - "height_no_outlier_iqr = height_outlier.drop(index=outliers_iqr.index)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "urvTyUfHrVrJ", - "colab_type": "text" - }, - "source": [ - "Uma segunda abordagem é observar as estatísticas descritivas dos dados.\n", - "\n", - "Repare no histograma abaixo:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "bc_paOePfHJ5", - "colab_type": "code", - "outputId": "6840da1c-bae6-4465-8aa7-87f69928e182", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 726 - } - }, - "source": [ - "sns.distplot(height_outlier);" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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AXxCmAWDAmq225hYrlm7yON/kaFRzSxWZ7JsGgG0jTAPAgC0sV9Vqm7boTEtSOhVVo9nW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\neXwBHD7Xin3HGmHrdCFOp0ZJYTIKshMwI90w1MtsNOrR3j7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- "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jI9ToieVrisQ", - "colab_type": "text" - }, - "source": [ - "Dá para perceber que a maior parte dos dados concentra-se em torno da média (~ 1.7 m) e que apenas algumas observações encontram-se bastante distantes dela." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "q49-oFz4gBHs", - "colab_type": "code", - "outputId": "f968b883-a1e3-4ead-963a-19d9f25e9d9e", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "height_outlier_mean = height_outlier.mean()\n", - "height_outlier_std = height_outlier.std()\n", - "\n", - "height_outlier_mean, height_outlier_std" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(1.7181251474953014, 0.2948590174540895)" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 56 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dTtLF6P2rvIh", - "colab_type": "text" - }, - "source": [ - "Um jeito de procurar por _outliers_ é ver quem se encontra fora do intervalo $[\\bar{x} - k * \\sigma, \\bar{x} + k * \\sigma]$, onde $k$ geralmente é 1.5, 2.0, 2.5 ou até 3.0.\n", - "\n", - "Abaixo utilizamos o $k = 2$, pois esse valor faz sentido (alturas menores que 1.12 m ou maiores que 2.30 m fogem do nosso padrão):" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "cI8gL-QrgK1s", - "colab_type": "code", - "outputId": "6c472ac1-ea23-4dd3-b833-91969a62f92d", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "non_outlier_interval_dist = [height_outlier_mean - 2 * height_outlier_std, height_outlier_mean + 2 * height_outlier_std]\n", - "\n", - "non_outlier_interval_dist" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[1.1284071125871225, 2.3078431824034804]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 57 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b5A37brPsVPw", - "colab_type": "text" - }, - "source": [ - "Novamente, conhecendo o intervalo, podemos identificar as observações que caem foram dele e removê-las:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "W6jVe5TMglf5", - "colab_type": "code", - "outputId": "c270dcb7-d46a-4dd8-94b3-c3d610269282", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 104 - } - }, - "source": [ - "outliers_dist = height_outlier[(height_outlier < non_outlier_interval_dist[0]) | (height_outlier > non_outlier_interval_dist[1])]\n", - "\n", - "outliers_dist" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "29 0.516665\n", - "38 2.943781\n", - "48 1.058498\n", - "68 2.737088\n", - "Name: Height, dtype: float64" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 58 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "jqYD2d3chJTK", - "colab_type": "code", - "colab": {} - }, - "source": [ - "height_no_outlier_dist = height_outlier.drop(index=outliers_dist.index)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8IL5fWP1sePM", - "colab_type": "text" - }, - "source": [ - "Até agora, nossas métodos de identificação de _outlier_ foram baseadas em estatísticas descritivas do nosso _data set_ (quantis, média e variância). Porém, alguns testes de hipóteses também existem.\n", - "\n", - "Um deles é o teste de Grubb. Esse é um teste bastante simples, cuja estatística de teste $G$ depende dos valores extremos do conjunto e da média amostral:\n", - "\n", - "$$G = \\frac{\\vert x_{\\text{\\{min ou max\\}}} - \\bar{x}\\vert}{s}$$\n", - "\n", - "onde $\\bar{x}$ é a média amostral e $s$ é o desvio-padrão da amostra.\n", - "\n", - "A hipótese nula, $H_{0}$, é de que não existem _outliers_ no _data set_. O teste de Grubb assume que os dados originam-se de uma distribuição normal, então pode ser válido testar essa hipótese antes.\n", - "\n", - "Rejeitamos a hipótese nula se o valor de $G$ encontrado for superior ao valor crítico do teste, que é dado por\n", - "\n", - "$$G_{\\text{crítico}} = \\frac{n - 1}{\\sqrt{n}} \\sqrt{\\frac{t_{\\alpha',n-2}^{2}}{n - 2 + t_{\\alpha',n-2}^{2}}}$$\n", - "\n", - "onde $n$ é o tamanho da amostra, $t$ é um valor com distribuição t-Student e $\\alpha'$ é $\\alpha/2n$ se o teste for bilateral (procuramos _outliers_ muito acima ou muito abaixo) ou $\\alpha/n$ se o teste for unilateral (acreditamos que o _outlier_, se houver, está em somente uma das extremidades da distribuição)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RNveH7ftxMOV", - "colab_type": "text" - }, - "source": [ - "Abaixo criamos algumas funções que nos auxiliam nos cálculos e na exibição dos resultados:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Ir61-q0ckV6K", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def grubb_test(g, n, alpha=0.05, tailed='two-tailed'):\n", - " if tailed == 'two-tailed':\n", - " critical = ((n - 1)/sqrt(n)) * sqrt(sct.t.isf(alpha/(2*n), n-2)**2/(n - 2 + sct.t.isf(alpha/(2*n), n-2)**2))\n", - " \n", - " return (g, critical, g > critical)\n", - " elif tailed == 'one-tailed':\n", - " critical = ((n - 1)/sqrt(n)) * sqrt(sct.t.isf(alpha/(n), n-2)**2/(n - 2 + sct.t.isf(alpha/(n), n-2)**2))\n", - " \n", - " return (g, critical, g > critical)\n", - " else:\n", - " raise ValueError(f\"Invalid tailed argument\")" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "c--VvSPuuHaM", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def grubb_summary(result, decimals=10):\n", - " return (\n", - " f\"Null hypothesis: there is no outliers in the data set\\n\"\n", - " f\"Test statistic: {np.round(result[0], decimals)}, \"\n", - " f\"Grubb's critical value: {np.round(result[1], decimals)}, \"\n", - " f\"Reject: {result[2]}\"\n", - " )" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "d8nFGEVuqgdC", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def next_outlier_candidate(data):\n", - " sample_distances = (data - data.mean()).abs()\n", - " candidate_idx = sample_distances.idxmax()\n", - " candidate_value = data[candidate_idx]\n", - " candidate_statistic = sample_distances.max()/data.std()\n", - " \n", - " return (candidate_idx, candidate_value, candidate_statistic, len(data))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MRZwuyOOxU7U", - "colab_type": "text" - }, - "source": [ - "Ao executarmos o teste de Grubb no nosso conjunto de alturas, encontramos alguns valores onde a hipótese nula é rejeitada, ou seja, há evidência de que o valor extremo é um _outlier_." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Rz-yVWFlt-M6", - "colab_type": "code", - "outputId": "cb11e99b-2195-45d7-9089-fdf292a65e1c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 434 - } - }, - "source": [ - "height_outlier_grubb = height_outlier.copy()\n", - "outliers_grubb = pd.Series()\n", - "has_outlier = True\n", - "\n", - "while has_outlier:\n", - " outlier_candidate = next_outlier_candidate(height_outlier_grubb)\n", - "\n", - " print(f\"Index: {outlier_candidate[0]}, \"\n", - " f\"Value: {np.round(outlier_candidate[1], 3)}, \"\n", - " f\"Test statistic: {np.round(outlier_candidate[2], 3)}, \"\n", - " f\"Sample size: {outlier_candidate[3]}\\n\")\n", - "\n", - " result = grubb_test(outlier_candidate[2], outlier_candidate[3])\n", - "\n", - " print(grubb_summary(result, 3))\n", - "\n", - " has_outlier = result[2]\n", - "\n", - " if has_outlier:\n", - " height_outlier_grubb = height_outlier_grubb.drop(index=outlier_candidate[0])\n", - " outliers_grubb.at[outlier_candidate[0]] = outlier_candidate[1]\n", - " \n", - " print(f\"\\n\")" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Index: 38, Value: 2.944, Test statistic: 4.157, Sample size: 100\n", - "\n", - "Null hypothesis: there is no outliers in the data set\n", - "Test statistic: 4.157, Grubb's critical value: 3.384, Reject: True\n", - "\n", - "\n", - "Index: 29, Value: 0.517, Test statistic: 4.421, Sample size: 99\n", - "\n", - "Null hypothesis: there is no outliers in the data set\n", - "Test statistic: 4.421, Grubb's critical value: 3.381, Reject: True\n", - "\n", - "\n", - "Index: 68, Value: 2.737, Test statistic: 4.219, Sample size: 98\n", - "\n", - "Null hypothesis: there is no outliers in the data set\n", - "Test statistic: 4.219, Grubb's critical value: 3.377, Reject: True\n", - "\n", - "\n", - "Index: 48, Value: 1.058, Test statistic: 2.96, Sample size: 97\n", - "\n", - "Null hypothesis: there is no outliers in the data set\n", - "Test statistic: 2.96, Grubb's critical value: 3.374, Reject: False\n", - "\n", - "\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "49MMneSg-DCj", - "colab_type": "code", - "outputId": "a98df152-223e-43e1-ced9-d113a40b879f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 86 - } - }, - "source": [ - "outliers_grubb" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "38 2.943781\n", - "29 0.516665\n", - "68 2.737088\n", - "dtype: float64" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 64 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_hajYam661Zd", - "colab_type": "text" - }, - "source": [ - "Abaixo comparamos os _outliers_ encontrados por cada um dos três métodos:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "l3P2Bavg-zMK", - "colab_type": "code", - "outputId": "25065774-49a4-4509-fe92-70a4d32c8cd2", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 86 - } - }, - "source": [ - "outliers = pd.Series({\"IQR\": outliers_iqr.index.values,\n", - " \"Normal\": outliers_dist.index.values,\n", - " \"Grubb\": outliers_grubb.index.values})\n", - "\n", - "outliers.apply(np.sort)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "IQR [29, 38, 48, 68, 91, 92]\n", - "Normal [29, 38, 48, 68]\n", - "Grubb [29, 38, 68]\n", - "dtype: object" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 65 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1oMEwGs_DHJW", - "colab_type": "text" - }, - "source": [ - "## _Features_ de texto\n", - "\n", - "Dados textuais são muito ricos e muito fáceis de serem encontrados. Diversos _data sets_ são compostos por documentos textuais e ainda um simples _scrapper_ pode coletar dezenas de milhares de documentos da Internet. Coleções de documentos são frequentemente chamadas de _corpus_ (plural, _corpora_).\n", - "\n", - "Nosso objetivo aqui é somente mostrar como preprocessar de forma simples _features_ textuais. Para isso, utilizaremos o _data set_ 20 newsgroups, que contém milhares de documentos categorizados em 20 grupos (desde astronomia até carros)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XItMVwyq8Dp9", - "colab_type": "text" - }, - "source": [ - "Abaixo escolhemos somente três grupos para restringir nosso escopo:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "usWrDfLvMNxw", - "colab_type": "code", - "colab": {} - }, - "source": [ - "categories = [\"sci.crypt\", \"sci.med\", \"sci.space\"]\n", - "\n", - "newsgroups = fetch_20newsgroups(subset=\"train\", categories=categories, shuffle=True, random_state=42)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4uNwK5uREAn7", - "colab_type": "text" - }, - "source": [ - "Temos agora um _corpus_ com 1782 documentos:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "_lUWgt06EtnR", - "colab_type": "code", - "outputId": "f82dd8b7-5f76-477c-9173-ee35d0c7e0aa", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "len(newsgroups.data)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "1782" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 67 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xh326fr28Jyc", - "colab_type": "text" - }, - "source": [ - "Um exemplo de documento desse _corpus_ é mostrado abaixo:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "vsfaD72_M52H", - "colab_type": "code", - "outputId": "fb895197-8753-49e6-a631-e7716ad8c8ee", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 295 - } - }, - "source": [ - "document_idx = 4\n", - "documents_total = len(newsgroups.data)\n", - "\n", - "print(f\"> Document {document_idx} of {documents_total}:\\n\\n{newsgroups.data[document_idx]}\")\n", - "print(f\"> Category: {newsgroups.target_names[newsgroups.target[document_idx]]}\")" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "> Document 4 of 1782:\n", - "\n", - "From: billc@col.hp.com (Bill Claussen)\n", - "Subject: Re: Should I be angry at this doctor?\n", - "Organization: HP Colorado Springs Division\n", - "Lines: 5\n", - "Distribution: na\n", - "NNTP-Posting-Host: hpcspe17.col.hp.com\n", - "\n", - "\n", - "Report them to your local BBB (Better Business Bureau).\n", - "\n", - "Bill Claussen\n", - "\n", - "\n", - "> Category: sci.med\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6liTZFzv8Nas", - "colab_type": "text" - }, - "source": [ - "Quando trabalhando com dados textuais, uma representação simples é ter:\n", - "\n", - "* Cada documento em uma linha.\n", - "* Cada palavra (ou termo) em uma coluna.\n", - "\n", - "Por exemplo, se nosso vocábulário (conjunto de todas palavras ou termos do _corpus_) tiver tamanho 10000 e tivermos 100 documentos, então nosso _data set_ será composto de 100 linhas e 10000 colunas." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qLBi7mFU8mLI", - "colab_type": "text" - }, - "source": [ - "O valor de cada célula, $x_{i, j}$, (interseção da linha $i$ com a coluna $j$) do _data set_ depende da tranformação que aplicarmos.\n", - "\n", - "A transformação mais simples é a contagem de palavras no documento, ou seja, $x_{i, j}$ indica o número de ocorrências da palavra $j$ no documento $i$.\n", - "\n", - "Isso pode ser obtido no sklearn pelo `CountVectorizer`:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "4E6FmUUhNs8b", - "colab_type": "code", - "colab": {} - }, - "source": [ - "count_vectorizer = CountVectorizer()\n", - "newsgroups_counts = count_vectorizer.fit_transform(newsgroups.data)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "TSylOCPKjLmh", - "colab_type": "code", - "outputId": "d7b6e6b8-f227-4ec5-a34a-2cf93fc8ebb5", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "type(newsgroups_counts)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "scipy.sparse.csr.csr_matrix" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 78 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M4rtFrsF9CgR", - "colab_type": "text" - }, - "source": [ - "Abaixo escolhemos dez palavras contidas no _corpus_ para exemplificar:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "kmxzJhkSUpIZ", - "colab_type": "code", - "outputId": "613a8241-c25e-4d5d-9830-1cee04671fc4", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - } - }, - "source": [ - "words_idx = sorted([count_vectorizer.vocabulary_.get(f\"{word.lower()}\") for word in\n", - " [u\"clipper\", u\"Kapor\",\n", - " u\"monitor\", u\"gibberish\",\n", - " u\"Banks\", u\"private\",\n", - " u\"study\", u\"group\",\n", - " u\"Colorado\", u\"Business\"]])\n", - "\n", - "pd.DataFrame(newsgroups_counts[:5, words_idx].toarray(), columns=np.array(count_vectorizer.get_feature_names())[words_idx])" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " banks business clipper colorado ... kapor monitor private study\n", - "0 0 0 2 0 ... 1 0 0 0\n", - "1 0 0 0 0 ... 0 2 0 0\n", - "2 3 0 0 0 ... 0 0 1 0\n", - "3 0 0 0 0 ... 0 0 0 2\n", - "4 0 1 0 1 ... 0 0 0 0\n", - "\n", - "[5 rows x 10 columns]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 70 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "C7WuoRgP9WE9", - "colab_type": "text" - }, - "source": [ - "Por exemplo, o valor 2 na interseção do documento 0 com a coluna `clipper` indica que a palavra _clipper_ aparece duas vezes no documento 0. Obviamente é possível que uma mesma palavra apareça em múltiplos documentos e mais óbvio ainda que um documento contenha múltiplas palavras." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UQzj-_QT9p7e", - "colab_type": "text" - }, - "source": [ - "O problema com essa abordagem é que não temos como medir relevância dos termos. E se o termo é super comum e aparece em quase todos documentos? E se o termo aparece muitas vezes no mesmo documento, mas poucas vezes nos outros?\n", - "\n", - "Essas perguntas não podem ser respondidas simplesmente com a contagem de termos acima. Para isso, precisamos do tf-idf." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AXBnOFk___QK", - "colab_type": "text" - }, - "source": [ - "O tf-idf é uma estatística baseada no _corpus_ composta de outras duas estatísticas:\n", - "\n", - "* $\\text{tf}(t, d)$, ou _term frequency_, é uma medida de quantas vezes o termo $t$ aparece no documento $d$. Algumas opções estão disponíveis, mas a mais simples é a contagem do número de ocorrências do termo no documento, $f_{t, d}$, exatamente o que computamos acima. Essa é a forma como sklearn define $tf$:\n", - "\n", - "$$\\text{tf}(t, d) = f_{t, d}$$\n", - "\n", - "* $\\text{idf}(t)$, ou _inverse document frequency_, é uma medida de relevância do termo em todos documentos do _corpus_. O sklearn a computa, seguindo valores _default_, da seguinte forma:\n", - "\n", - "$$\\text{idf}(t) = \\log{\\frac{1+n}{1 + d_{t}}} + 1$$\n", - "\n", - "onde $n$ é o número de documentos no _corpus_ e $d_{t}$ é o número de documentos no _corpus_ que contêm o termo $t$ ($0 < d_{t} \\leq n$).\n", - "\n", - "O tf-idf é calculado multiplicando esses dois valores:\n", - "\n", - "$$\\text{tf-idf}(t, d) = \\text{tf}(t, d) \\times \\text{idf}(t) = f_{t, d} \\times \\log{\\frac{1+n}{1 + d_{t}}} + 1$$\n", - "\n", - "O sklearn também normaliza todos documentos resultantes, ou seja todas linhas da matriz, para terem norma unitária. Em outras palavras, os elementos do vetor de tf-idf do documento $i$ são dados por:\n", - "\n", - "$$\\text{tf-idf}(i, j)_{\\text{normalizado}} = \\frac{\\text{tf-idf}(i, j)}{\\sqrt{\\text{tf-idf}(i, 1)^{2} + \\text{tf-idf}(i, 2)^{2} + \\cdots + \\text{tf-idf}(i, T)^{2}}}$$\n", - "\n", - "onde $T$ é o número de termos do _corpus_, ou seja, o tamanho do vocabulário." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bWpYWUMjCH8l", - "colab_type": "text" - }, - "source": [ - "O tf-idf é sempre um valor não negativo e quanto mais alto, maior a relevância do termo.\n", - "\n", - "Note como o tf aumenta de acordo com o número de ocorrências do termo no documento: quanto mais frequente o termo, mas relevante ele parece ser.\n", - "\n", - "O idf é uma medida de \"raridade\" do termo através de todo _corpus_: quanto mais alto, menos o termo aparece no _corpus_ e consequentemente mais informação ele traz.\n", - "\n", - "Multiplicando os dois, temos uma medida do quão relevante aquele termo é para aquele documento no _corpus_." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b_N2VQnwDaey", - "colab_type": "text" - }, - "source": [ - "O sklearn provê um transformador, `TfidfTransformer`, que transforma de uma matriz de frequências, como a retornada pelo `CountVectorizer`, e retorna uma matriz de tf-idf:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Fyxgx0YhVwtF", - "colab_type": "code", - "colab": {} - }, - "source": [ - "tfidf_transformer = TfidfTransformer()\n", - "\n", - "tfidf_transformer.fit(newsgroups_counts)\n", - "\n", - "newsgroups_tfidf = tfidf_transformer.transform(newsgroups_counts)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "evk8smtLWNtO", - "colab_type": "code", - "outputId": "bf99b51a-e276-480c-dee9-13713e85a00b", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - } - }, - "source": [ - "pd.DataFrame(newsgroups_tfidf[:5, words_idx].toarray(), columns=np.array(count_vectorizer.get_feature_names())[words_idx])" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " banks business clipper ... monitor private study\n", - "0 0.000000 0.000000 0.081293 ... 0.000000 0.000000 0.000000\n", - "1 0.000000 0.000000 0.000000 ... 0.179352 0.000000 0.000000\n", - "2 0.148152 0.000000 0.000000 ... 0.000000 0.048551 0.000000\n", - "3 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.083477\n", - "4 0.000000 0.117248 0.000000 ... 0.000000 0.000000 0.000000\n", - "\n", - "[5 rows x 10 columns]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 72 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "h9hI18kYDsuA", - "colab_type": "text" - }, - "source": [ - "Também podemos obter a matriz de tf-idf diretamente do _corpus_ sem ter que passar pela matriz de frequência com o transformador `TfidfVectorizer`:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "wPV4xrxzWlA-", - "colab_type": "code", - "colab": {} - }, - "source": [ - "tfidf_vectorizer = TfidfVectorizer()\n", - "\n", - "tfidf_vectorizer.fit(newsgroups.data)\n", - "\n", - "newsgroups_tfidf_vectorized = tfidf_vectorizer.transform(newsgroups.data)" + "text/plain": [ + " Height_n Score_n Height_n^2 ... Math Physics Unknown\n", + "0 0.265531 0.485549 0.070507 ... 0 0 0\n", + "1 0.490982 0.448940 0.241063 ... 0 0 0\n", + "2 0.421844 0.488439 0.177952 ... 0 0 1\n", + "3 0.556112 0.202312 0.309261 ... 0 0 0\n", + "4 0.366733 0.450867 0.134493 ... 0 1 0\n", + "5 0.505010 0.488439 0.255035 ... 0 1 0\n", + "6 0.405812 0.734104 0.164683 ... 0 0 0\n", + "7 0.330661 0.514451 0.109337 ... 0 1 0\n", + "8 0.410822 0.645472 0.168774 ... 0 1 0\n", + "9 0.333667 0.369942 0.111334 ... 0 1 0\n", + "\n", + "[10 rows x 9 columns]" + ] + }, + "execution_count": 48, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "data_transformed = pd.concat([height_score_normalized_poly, course_discretized], axis=1)\n", + "\n", + "data_transformed.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1NLD-pyliXWO" + }, + "source": [ + "Vale ressaltar que:\n", + "\n", + "* Poderíamos utilizar também o `ColumnTransformer` para compor (por isso, ele se encontra no módulo `sklearn.compose`) múltiplos `Pipeline` em diferentes variáveis.\n", + "* Os `Pipeline` não servem apenas para a transformação dos dados de treinamento. Eles também podem (e devem) ser usados para submeter os dados de teste e até de produção aos mesmos procedimentos dos dados de treinamento." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "SbShR7kMZGwE" + }, + "source": [ + "## _Outliers_\n", + "\n", + "_Outliers_, os famosos \"pontos fora da curva\", são observações que não parecem seguir o mesmo padrão dos demais dados. Eles podem vir de distribuições diferentes, serem erros na coleta de dados, erros de medição etc.\n", + "\n", + "Eles influenciam nossas análises e os nossos algoritmos ao apresentar comportamento distoante do resto do _data set_, impactando na média, variância, funções de perda e custo etc. Se fizer sentido, eles devem ser removidos ou transformados antes de prosseguirmos com a análise.\n", + "\n", + "No entanto, devemos julgar com cautela sua remoção: __alguns _outliers_ são dados autênticos e devem ser estudados com atenção__. Por exemplo, a remoção de uma medição muito alta na temperatura de um reator seria um erro, pois essa medição pode estar nos indicando um potencial problema com o dispositivo.\n", + "\n", + "Abaixo estudamos algumas técnicas simples para encontrar _outliers_.\n", + "\n", + "![outlier](https://www.stats4stem.org/common/web/plugins/ckeditor/plugins/doksoft_uploader/userfiles/WithInfOutlier.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "u3bsTDv0pAN4" + }, + "source": [ + "Começamos criando uma cópia da variável `Height` do nosso _data set_ para não impactar o original:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "tQ7AQztcZkYx" + }, + "outputs": [], + "source": [ + "height_outlier = data.Height.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "VQNHBAu4pHcp" + }, + "source": [ + "Adicionamos dez _outliers_ que representam pessoas estranhamente baixas ou estranhamente altas para o padrão que estamos observando:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 208 + }, + "colab_type": "code", + "id": "nX2R3V0HZI0w", + "outputId": "6acbd63c-820e-485a-cde4-72a69fefe13d" + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'pd' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mheight_outlier_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight_outlier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mtoo_short_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight_outlier_idx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mtoo_tall_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight_outlier_idx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined" + ] + } + ], + "source": [ + "height_outlier_idx = pd.Index(np.random.choice(height_outlier.index, 10, replace=False))\n", + "\n", + "too_short_idx = pd.Index(height_outlier_idx[:5])\n", + "too_tall_idx = pd.Index(height_outlier_idx[5:])\n", + "\n", + "height_outlier[too_short_idx] = np.random.normal(loc=1.30, scale=0.5, size=5)\n", + "height_outlier[too_tall_idx] = np.random.normal(loc=2.20, scale=0.5, size=5)\n", + "\n", + "outlier_idx = too_short_idx | too_tall_idx\n", + "\n", + "height_outlier[outlier_idx]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mwNbTzDnpoDL" + }, + "source": [ + "Note que nem todos dados gerados se tornaram realmente _outliers_. Como geramos de uma distribuição aleatória, corremos esse risco.\n", + "\n", + "No entanto, temos alguns dados estranhos como 0.51 m e 2.73 m." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "x5pwD_1EqRNZ" + }, + "source": [ + "No _boxplot_ padrão, os dados mais extremos são mostrados como pontos fora do alcance dos _whiskers_ (as barrinhas do _box plot_).\n", + "\n", + "No caso abaixo, notamos três pontos acima e três pontos abaixo do considerado \"dentro da faixa normal\"." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 695 + }, + "colab_type": "code", + "id": "hRMVhYz3b2KH", + "outputId": "9e090cef-804c-4f17-958b-5e25154662db" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(height_outlier, orient=\"vertical\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MOKP49JMqTog" + }, + "source": [ + "Uma primeira abordagem bem simples é encontrar os pontos do _box plot_ acima.\n", + "\n", + "Tudo que estiver fora da faixa $[Q1 - 1.5 \\times \\text{IQR}, Q3 + 1.5 \\times \\text{IQR}]$ é considerado um ponto anômalo para aquele padrão:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "z_h0zaVDce0N", + "outputId": "86b9e772-6438-4820-87ba-dab83a4b1dd8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Faixa considerada \"normal\": [1.18575, 2.24175]\n" + ] + } + ], + "source": [ + "q1 = height_outlier.quantile(0.25)\n", + "q3 = height_outlier.quantile(0.75)\n", + "iqr = q3 - q1\n", + "\n", + "non_outlier_interval_iqr = [q1 - 1.5 * iqr, q3 + 1.5 * iqr]\n", + "\n", + "print(f\"Faixa considerada \\\"normal\\\": {non_outlier_interval_iqr}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wsuVvr8hq4Rc" + }, + "source": [ + "Agora podemos identificar quais pontos encontram-se fora desse intervalo, ou seja, podem ser considerados _outliers_:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "colab_type": "code", + "id": "hm78PWbhc9Dz", + "outputId": "ee3995ea-8a63-4c90-b3dd-57ba673887ee" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "29 0.516665\n", + "38 2.943781\n", + "48 1.058498\n", + "68 2.737088\n", + "91 2.272000\n", + "92 1.164000\n", + "Name: Height, dtype: float64" + ] + }, + "execution_count": 53, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "outliers_iqr = height_outlier[(height_outlier < non_outlier_interval_iqr[0]) | (height_outlier > non_outlier_interval_iqr[1])]\n", + "\n", + "outliers_iqr" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XcF70kmerGEq" + }, + "source": [ + "Se estivermos seguos de que esses pontos representam de fato _outliers_ e que sua remoção não traz prejuízo à nossa análise, então podemos removê-los:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "BVRJS9DNeb9z" + }, + "outputs": [], + "source": [ + "height_no_outlier_iqr = height_outlier.drop(index=outliers_iqr.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "urvTyUfHrVrJ" + }, + "source": [ + "Uma segunda abordagem é observar as estatísticas descritivas dos dados.\n", + "\n", + "Repare no histograma abaixo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 726 + }, + "colab_type": "code", + "id": "bc_paOePfHJ5", + "outputId": "6840da1c-bae6-4465-8aa7-87f69928e182" + }, + "outputs": [ + { + "data": { + "image/png": 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AXxCmAWDAmq225hYrlm7yON/kaFRzSxWZ7JsGgG0jTAPAgC0sV9Vqm7boTEtSOhVVo9nW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\neXwBHD7Xin3HGmHrdCFOp0ZJYTIKshMwI90w1MtsNOrR3j7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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(height_outlier);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jI9ToieVrisQ" + }, + "source": [ + "Dá para perceber que a maior parte dos dados concentra-se em torno da média (~ 1.7 m) e que apenas algumas observações encontram-se bastante distantes dela." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "q49-oFz4gBHs", + "outputId": "f968b883-a1e3-4ead-963a-19d9f25e9d9e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.7181251474953014, 0.2948590174540895)" + ] + }, + "execution_count": 56, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "height_outlier_mean = height_outlier.mean()\n", + "height_outlier_std = height_outlier.std()\n", + "\n", + "height_outlier_mean, height_outlier_std" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dTtLF6P2rvIh" + }, + "source": [ + "Um jeito de procurar por _outliers_ é ver quem se encontra fora do intervalo $[\\bar{x} - k * \\sigma, \\bar{x} + k * \\sigma]$, onde $k$ geralmente é 1.5, 2.0, 2.5 ou até 3.0.\n", + "\n", + "Abaixo utilizamos o $k = 2$, pois esse valor faz sentido (alturas menores que 1.12 m ou maiores que 2.30 m fogem do nosso padrão):" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "cI8gL-QrgK1s", + "outputId": "6c472ac1-ea23-4dd3-b833-91969a62f92d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.1284071125871225, 2.3078431824034804]" + ] + }, + "execution_count": 57, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "non_outlier_interval_dist = [height_outlier_mean - 2 * height_outlier_std, height_outlier_mean + 2 * height_outlier_std]\n", + "\n", + "non_outlier_interval_dist" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b5A37brPsVPw" + }, + "source": [ + "Novamente, conhecendo o intervalo, podemos identificar as observações que caem foram dele e removê-las:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 104 + }, + "colab_type": "code", + "id": "W6jVe5TMglf5", + "outputId": "c270dcb7-d46a-4dd8-94b3-c3d610269282" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "29 0.516665\n", + "38 2.943781\n", + "48 1.058498\n", + "68 2.737088\n", + "Name: Height, dtype: float64" + ] + }, + "execution_count": 58, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "outliers_dist = height_outlier[(height_outlier < non_outlier_interval_dist[0]) | (height_outlier > non_outlier_interval_dist[1])]\n", + "\n", + "outliers_dist" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "jqYD2d3chJTK" + }, + "outputs": [], + "source": [ + "height_no_outlier_dist = height_outlier.drop(index=outliers_dist.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8IL5fWP1sePM" + }, + "source": [ + "Até agora, nossas métodos de identificação de _outlier_ foram baseadas em estatísticas descritivas do nosso _data set_ (quantis, média e variância). Porém, alguns testes de hipóteses também existem.\n", + "\n", + "Um deles é o teste de Grubb. Esse é um teste bastante simples, cuja estatística de teste $G$ depende dos valores extremos do conjunto e da média amostral:\n", + "\n", + "$$G = \\frac{\\vert x_{\\text{\\{min ou max\\}}} - \\bar{x}\\vert}{s}$$\n", + "\n", + "onde $\\bar{x}$ é a média amostral e $s$ é o desvio-padrão da amostra.\n", + "\n", + "A hipótese nula, $H_{0}$, é de que não existem _outliers_ no _data set_. O teste de Grubb assume que os dados originam-se de uma distribuição normal, então pode ser válido testar essa hipótese antes.\n", + "\n", + "Rejeitamos a hipótese nula se o valor de $G$ encontrado for superior ao valor crítico do teste, que é dado por\n", + "\n", + "$$G_{\\text{crítico}} = \\frac{n - 1}{\\sqrt{n}} \\sqrt{\\frac{t_{\\alpha',n-2}^{2}}{n - 2 + t_{\\alpha',n-2}^{2}}}$$\n", + "\n", + "onde $n$ é o tamanho da amostra, $t$ é um valor com distribuição t-Student e $\\alpha'$ é $\\alpha/2n$ se o teste for bilateral (procuramos _outliers_ muito acima ou muito abaixo) ou $\\alpha/n$ se o teste for unilateral (acreditamos que o _outlier_, se houver, está em somente uma das extremidades da distribuição)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RNveH7ftxMOV" + }, + "source": [ + "Abaixo criamos algumas funções que nos auxiliam nos cálculos e na exibição dos resultados:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Ir61-q0ckV6K" + }, + "outputs": [], + "source": [ + "def grubb_test(g, n, alpha=0.05, tailed='two-tailed'):\n", + " if tailed == 'two-tailed':\n", + " critical = ((n - 1)/sqrt(n)) * sqrt(sct.t.isf(alpha/(2*n), n-2)**2/(n - 2 + sct.t.isf(alpha/(2*n), n-2)**2))\n", + " \n", + " return (g, critical, g > critical)\n", + " elif tailed == 'one-tailed':\n", + " critical = ((n - 1)/sqrt(n)) * sqrt(sct.t.isf(alpha/(n), n-2)**2/(n - 2 + sct.t.isf(alpha/(n), n-2)**2))\n", + " \n", + " return (g, critical, g > critical)\n", + " else:\n", + " raise ValueError(f\"Invalid tailed argument\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "c--VvSPuuHaM" + }, + "outputs": [], + "source": [ + "def grubb_summary(result, decimals=10):\n", + " return (\n", + " f\"Null hypothesis: there is no outliers in the data set\\n\"\n", + " f\"Test statistic: {np.round(result[0], decimals)}, \"\n", + " f\"Grubb's critical value: {np.round(result[1], decimals)}, \"\n", + " f\"Reject: {result[2]}\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "d8nFGEVuqgdC" + }, + "outputs": [], + "source": [ + "def next_outlier_candidate(data):\n", + " sample_distances = (data - data.mean()).abs()\n", + " candidate_idx = sample_distances.idxmax()\n", + " candidate_value = data[candidate_idx]\n", + " candidate_statistic = sample_distances.max()/data.std()\n", + " \n", + " return (candidate_idx, candidate_value, candidate_statistic, len(data))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MRZwuyOOxU7U" + }, + "source": [ + "Ao executarmos o teste de Grubb no nosso conjunto de alturas, encontramos alguns valores onde a hipótese nula é rejeitada, ou seja, há evidência de que o valor extremo é um _outlier_." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 434 + }, + "colab_type": "code", + "id": "Rz-yVWFlt-M6", + "outputId": "cb11e99b-2195-45d7-9089-fdf292a65e1c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index: 38, Value: 2.944, Test statistic: 4.157, Sample size: 100\n", + "\n", + "Null hypothesis: there is no outliers in the data set\n", + "Test statistic: 4.157, Grubb's critical value: 3.384, Reject: True\n", + "\n", + "\n", + "Index: 29, Value: 0.517, Test statistic: 4.421, Sample size: 99\n", + "\n", + "Null hypothesis: there is no outliers in the data set\n", + "Test statistic: 4.421, Grubb's critical value: 3.381, Reject: True\n", + "\n", + "\n", + "Index: 68, Value: 2.737, Test statistic: 4.219, Sample size: 98\n", + "\n", + "Null hypothesis: there is no outliers in the data set\n", + "Test statistic: 4.219, Grubb's critical value: 3.377, Reject: True\n", + "\n", + "\n", + "Index: 48, Value: 1.058, Test statistic: 2.96, Sample size: 97\n", + "\n", + "Null hypothesis: there is no outliers in the data set\n", + "Test statistic: 2.96, Grubb's critical value: 3.374, Reject: False\n", + "\n", + "\n" + ] + } + ], + "source": [ + "height_outlier_grubb = height_outlier.copy()\n", + "outliers_grubb = pd.Series()\n", + "has_outlier = True\n", + "\n", + "while has_outlier:\n", + " outlier_candidate = next_outlier_candidate(height_outlier_grubb)\n", + "\n", + " print(f\"Index: {outlier_candidate[0]}, \"\n", + " f\"Value: {np.round(outlier_candidate[1], 3)}, \"\n", + " f\"Test statistic: {np.round(outlier_candidate[2], 3)}, \"\n", + " f\"Sample size: {outlier_candidate[3]}\\n\")\n", + "\n", + " result = grubb_test(outlier_candidate[2], outlier_candidate[3])\n", + "\n", + " print(grubb_summary(result, 3))\n", + "\n", + " has_outlier = result[2]\n", + "\n", + " if has_outlier:\n", + " height_outlier_grubb = height_outlier_grubb.drop(index=outlier_candidate[0])\n", + " outliers_grubb.at[outlier_candidate[0]] = outlier_candidate[1]\n", + " \n", + " print(f\"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86 + }, + "colab_type": "code", + "id": "49MMneSg-DCj", + "outputId": "a98df152-223e-43e1-ced9-d113a40b879f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "38 2.943781\n", + "29 0.516665\n", + "68 2.737088\n", + "dtype: float64" + ] + }, + "execution_count": 64, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "outliers_grubb" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_hajYam661Zd" + }, + "source": [ + "Abaixo comparamos os _outliers_ encontrados por cada um dos três métodos:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86 + }, + "colab_type": "code", + "id": "l3P2Bavg-zMK", + "outputId": "25065774-49a4-4509-fe92-70a4d32c8cd2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "IQR [29, 38, 48, 68, 91, 92]\n", + "Normal [29, 38, 48, 68]\n", + "Grubb [29, 38, 68]\n", + "dtype: object" + ] + }, + "execution_count": 65, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "outliers = pd.Series({\"IQR\": outliers_iqr.index.values,\n", + " \"Normal\": outliers_dist.index.values,\n", + " \"Grubb\": outliers_grubb.index.values})\n", + "\n", + "outliers.apply(np.sort)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1oMEwGs_DHJW" + }, + "source": [ + "## _Features_ de texto\n", + "\n", + "Dados textuais são muito ricos e muito fáceis de serem encontrados. Diversos _data sets_ são compostos por documentos textuais e ainda um simples _scrapper_ pode coletar dezenas de milhares de documentos da Internet. Coleções de documentos são frequentemente chamadas de _corpus_ (plural, _corpora_).\n", + "\n", + "Nosso objetivo aqui é somente mostrar como preprocessar de forma simples _features_ textuais. Para isso, utilizaremos o _data set_ 20 newsgroups, que contém milhares de documentos categorizados em 20 grupos (desde astronomia até carros)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XItMVwyq8Dp9" + }, + "source": [ + "Abaixo escolhemos somente três grupos para restringir nosso escopo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "usWrDfLvMNxw" + }, + "outputs": [], + "source": [ + "categories = [\"sci.crypt\", \"sci.med\", \"sci.space\"]\n", + "\n", + "newsgroups = fetch_20newsgroups(subset=\"train\", categories=categories, shuffle=True, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4uNwK5uREAn7" + }, + "source": [ + "Temos agora um _corpus_ com 1782 documentos:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "_lUWgt06EtnR", + "outputId": "f82dd8b7-5f76-477c-9173-ee35d0c7e0aa" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1782" + ] + }, + "execution_count": 67, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "len(newsgroups.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "xh326fr28Jyc" + }, + "source": [ + "Um exemplo de documento desse _corpus_ é mostrado abaixo:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + }, + "colab_type": "code", + "id": "vsfaD72_M52H", + "outputId": "fb895197-8753-49e6-a631-e7716ad8c8ee" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> Document 4 of 1782:\n", + "\n", + "From: billc@col.hp.com (Bill Claussen)\n", + "Subject: Re: Should I be angry at this doctor?\n", + "Organization: HP Colorado Springs Division\n", + "Lines: 5\n", + "Distribution: na\n", + "NNTP-Posting-Host: hpcspe17.col.hp.com\n", + "\n", + "\n", + "Report them to your local BBB (Better Business Bureau).\n", + "\n", + "Bill Claussen\n", + "\n", + "\n", + "> Category: sci.med\n" + ] + } + ], + "source": [ + "document_idx = 4\n", + "documents_total = len(newsgroups.data)\n", + "\n", + "print(f\"> Document {document_idx} of {documents_total}:\\n\\n{newsgroups.data[document_idx]}\")\n", + "print(f\"> Category: {newsgroups.target_names[newsgroups.target[document_idx]]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6liTZFzv8Nas" + }, + "source": [ + "Quando trabalhando com dados textuais, uma representação simples é ter:\n", + "\n", + "* Cada documento em uma linha.\n", + "* Cada palavra (ou termo) em uma coluna.\n", + "\n", + "Por exemplo, se nosso vocábulário (conjunto de todas palavras ou termos do _corpus_) tiver tamanho 10000 e tivermos 100 documentos, então nosso _data set_ será composto de 100 linhas e 10000 colunas." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qLBi7mFU8mLI" + }, + "source": [ + "O valor de cada célula, $x_{i, j}$, (interseção da linha $i$ com a coluna $j$) do _data set_ depende da tranformação que aplicarmos.\n", + "\n", + "A transformação mais simples é a contagem de palavras no documento, ou seja, $x_{i, j}$ indica o número de ocorrências da palavra $j$ no documento $i$.\n", + "\n", + "Isso pode ser obtido no sklearn pelo `CountVectorizer`:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "4E6FmUUhNs8b" + }, + "outputs": [], + "source": [ + "count_vectorizer = CountVectorizer()\n", + "newsgroups_counts = count_vectorizer.fit_transform(newsgroups.data)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "TSylOCPKjLmh", + "outputId": "d7b6e6b8-f227-4ec5-a34a-2cf93fc8ebb5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "scipy.sparse.csr.csr_matrix" + ] + }, + "execution_count": 78, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "type(newsgroups_counts)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "M4rtFrsF9CgR" + }, + "source": [ + "Abaixo escolhemos dez palavras contidas no _corpus_ para exemplificar:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "colab_type": "code", + "id": "kmxzJhkSUpIZ", + "outputId": "613a8241-c25e-4d5d-9830-1cee04671fc4" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Obviamente é possível que uma mesma palavra apareça em múltiplos documentos e mais óbvio ainda que um documento contenha múltiplas palavras." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UQzj-_QT9p7e" + }, + "source": [ + "O problema com essa abordagem é que não temos como medir relevância dos termos. E se o termo é super comum e aparece em quase todos documentos? E se o termo aparece muitas vezes no mesmo documento, mas poucas vezes nos outros?\n", + "\n", + "Essas perguntas não podem ser respondidas simplesmente com a contagem de termos acima. Para isso, precisamos do tf-idf." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AXBnOFk___QK" + }, + "source": [ + "O tf-idf é uma estatística baseada no _corpus_ composta de outras duas estatísticas:\n", + "\n", + "* $\\text{tf}(t, d)$, ou _term frequency_, é uma medida de quantas vezes o termo $t$ aparece no documento $d$. Algumas opções estão disponíveis, mas a mais simples é a contagem do número de ocorrências do termo no documento, $f_{t, d}$, exatamente o que computamos acima. Essa é a forma como sklearn define $tf$:\n", + "\n", + "$$\\text{tf}(t, d) = f_{t, d}$$\n", + "\n", + "* $\\text{idf}(t)$, ou _inverse document frequency_, é uma medida de relevância do termo em todos documentos do _corpus_. O sklearn a computa, seguindo valores _default_, da seguinte forma:\n", + "\n", + "$$\\text{idf}(t) = \\log{\\frac{1+n}{1 + d_{t}}} + 1$$\n", + "\n", + "onde $n$ é o número de documentos no _corpus_ e $d_{t}$ é o número de documentos no _corpus_ que contêm o termo $t$ ($0 < d_{t} \\leq n$).\n", + "\n", + "O tf-idf é calculado multiplicando esses dois valores:\n", + "\n", + "$$\\text{tf-idf}(t, d) = \\text{tf}(t, d) \\times \\text{idf}(t) = f_{t, d} \\times \\log{\\frac{1+n}{1 + d_{t}}} + 1$$\n", + "\n", + "O sklearn também normaliza todos documentos resultantes, ou seja todas linhas da matriz, para terem norma unitária. Em outras palavras, os elementos do vetor de tf-idf do documento $i$ são dados por:\n", + "\n", + "$$\\text{tf-idf}(i, j)_{\\text{normalizado}} = \\frac{\\text{tf-idf}(i, j)}{\\sqrt{\\text{tf-idf}(i, 1)^{2} + \\text{tf-idf}(i, 2)^{2} + \\cdots + \\text{tf-idf}(i, T)^{2}}}$$\n", + "\n", + "onde $T$ é o número de termos do _corpus_, ou seja, o tamanho do vocabulário." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "bWpYWUMjCH8l" + }, + "source": [ + "O tf-idf é sempre um valor não negativo e quanto mais alto, maior a relevância do termo.\n", + "\n", + "Note como o tf aumenta de acordo com o número de ocorrências do termo no documento: quanto mais frequente o termo, mas relevante ele parece ser.\n", + "\n", + "O idf é uma medida de \"raridade\" do termo através de todo _corpus_: quanto mais alto, menos o termo aparece no _corpus_ e consequentemente mais informação ele traz.\n", + "\n", + "Multiplicando os dois, temos uma medida do quão relevante aquele termo é para aquele documento no _corpus_." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b_N2VQnwDaey" + }, + "source": [ + "O sklearn provê um transformador, `TfidfTransformer`, que transforma de uma matriz de frequências, como a retornada pelo `CountVectorizer`, e retorna uma matriz de tf-idf:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Fyxgx0YhVwtF" + }, + "outputs": [], + "source": [ + "tfidf_transformer = TfidfTransformer()\n", + "\n", + "tfidf_transformer.fit(newsgroups_counts)\n", + "\n", + "newsgroups_tfidf = tfidf_transformer.transform(newsgroups_counts)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "colab_type": "code", + "id": "evk8smtLWNtO", + "outputId": "bf99b51a-e276-480c-dee9-13713e85a00b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(1782, 33796)" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 75 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NjPMTtkUwrS1", - "colab_type": "text" - }, - "source": [ - "## Referências\n", - "\n", - "* [Feature engineering](https://jakevdp.github.io/PythonDataScienceHandbook/05.04-feature-engineering.html)\n", - "\n", - "* [Feature Scaling with scikit-learn](http://benalexkeen.com/feature-scaling-with-scikit-learn/)\n", - "\n", - "* [Anthony Goldbloom gives you the secret to winning Kaggle competitions](https://www.import.io/post/how-to-win-a-kaggle-competition/)\n", - "\n", - "* [What are some best practices in Feature Engineering?](https://www.quora.com/What-are-some-best-practices-in-Feature-Engineering)\n", - "\n", - "* [Discover Feature Engineering, How to Engineer Features and How to Get Good at It](https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/)\n", - "\n", - "* [Fundamental Techniques of Feature Engineering for Machine Learning](https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114)\n", - "\n", - "* [Feature Engineering Cookbook for Machine Learning](https://medium.com/@michaelabehsera/feature-engineering-cookbook-for-machine-learning-7bf21f0bcbae)\n", - "\n", - "* [A Simple Guide to Scikit-learn Pipelines](https://medium.com/vickdata/a-simple-guide-to-scikit-learn-pipelines-4ac0d974bdcf)\n", - "\n", - "* [Outlier detection with Scikit Learn](https://www.mikulskibartosz.name/outlier-detection-with-scikit-learn/)\n", - "\n", - "* [Working With Text Data](https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html)\n", - "\n", - "* [WTF is TF-IDF?](https://www.kdnuggets.com/2018/08/wtf-tf-idf.html)\n" - ] + "text/plain": [ + " banks business clipper ... monitor private study\n", + "0 0.000000 0.000000 0.081293 ... 0.000000 0.000000 0.000000\n", + "1 0.000000 0.000000 0.000000 ... 0.179352 0.000000 0.000000\n", + "2 0.148152 0.000000 0.000000 ... 0.000000 0.048551 0.000000\n", + "3 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.083477\n", + "4 0.000000 0.117248 0.000000 ... 0.000000 0.000000 0.000000\n", + "\n", + "[5 rows x 10 columns]" + ] + }, + "execution_count": 74, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" } - 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