product-solution.ipynb

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    "- [Otto Group Product Classification](#Otto-Group-Product-Classification)\n",
    "    - [Contexto](#Contexto)\n",
    "    - [Dados](#Dados)\n",
    "- [Benchmark](#Benchmark)\n",
    "- [Cross Validation](#Cross-Validation)\n",
    "- [Tratamento](#Tratamento)\n",
    "    - [Correlação](#Correlação)\n",
    "    - [Filtrando colunas](#Filtrando-colunas)\n",
    "    - [Resultado](#Resultado)\n",
    "- [Train/Test split](#Train/Test-split)\n",
    "    - [Feature Scaling](#Feature-Scaling)\n",
    "    - [Confusion Matrix](#Confusion-Matrix)\n",
    "- [Modelo Dummy Classifier](#Modelo-Dummy-Classifier)\n",
    "- [Boosting](#Boosting)\n",
    "- [Gradient Descent](#Gradient-Descent)\n",
    "- [XGBoost](#XGBoost)\n",
    "    - [Model](#Model)\n",
    "        - [Objective Function:](#Objective-Function:)\n",
    "        - [CART](#CART)\n",
    "    - [Training](#Training)\n",
    "        - [Additive](#Additive)\n",
    "- [GridSearchCV](#GridSearchCV)\n",
    "    - [Aplicando GridSearchCV ao XGBClassifier:](#Aplicando-GridSearchCV-ao-XGBClassifier:)\n",
    "    - [Aplicando GridSearchCV ao Decision Tree Classifier:](#Aplicando-GridSearchCV-ao-Decision-Tree-Classifier:)\n",
    "- [Trees](#Trees)\n",
    "    - [Decision Tree](#Decision-Tree)\n",
    "    - [Distribuição dos dados](#Distribuição-dos-dados)\n",
    "        - [Filtrar dados](#Filtrar-dados)\n",
    "        - [Verificando resultado](#Verificando-resultado)\n",
    "- [Random Forest](#Random-Forest)\n",
    "    - [Utilizando o algoritmo](#Utilizando-o-algoritmo)\n",
    "    - [Verificando com Cross Validation](#Verificando-com-Cross-Validation)\n",
    "    - [Importancia das features para a RF](#Importancia-das-features-para-a-RF)\n",
    "        - [Gini](#Gini)\n",
    "            - [O indice](#O-indice)\n",
    "            - [Para Decisions Trees](#Para-Decisions-Trees)\n",
    "            - [Decrease Mean Importance](#Decrease-Mean-Importance)\n",
    "    - [ExtraTrees](#ExtraTrees)\n",
    "- [Neurônio Artificial](#Neurônio-Artificial)\n",
    "    - [Entrada](#Entrada)\n",
    "    - [Função agregadora](#Função-agregadora)\n",
    "    - [Neurônio](#Neurônio)\n",
    "    - [Formula](#Formula)\n",
    "    - [MLP Classifier](#MLP-Classifier)\n",
    "- [Conclusão](#Conclusão)\n",
    "- [Referências Bibliográficas](#Referências-Bibliográficas)"
   ]
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    "# Otto Group Product Classification\n",
    "\n",
    "Este notebook é uma proposta de solução utilizando técnicas de data-mining e machine learn para o problema de classificação de produtos da companhia Otto disponíveis em: [Kaggle (challenge): Otto group product classification](https://www.kaggle.com/c/otto-group-product-\n",
    "classification-challenge)\n",
    "\n",
    "## Contexto\n",
    "\n",
    "Retirado da descrição do problema, temos que o grupo Otto é uma das maiores companhias de *e-commerce* do mundo, e possui s filiais em mais de 20 paises. Vendem milhões de produtos ao redor do mundo todos os dias com centezas de produtos sendo adicionados constantemente.\n",
    "\n",
    "A análise de consistência da performance dos produtos deles é crucial, entretando, com a infraestrutura de escala global que possuem, produtos identicos são classifidados de maneira diferenciada. Entretanto a análise da qualidade dos produtos depende fortemente da acurácia na habilidade de agrupar produtos semelhantes. Quanto melhor for a classificação, mais intuitivamente eles ter um maior alcance com seus produtos.\n",
    "\n",
    "## Dados\n",
    "\n",
    "Foram disponibilizados 2 bases de dados separadas. A primeira delas contém 61878 registros com rótulo da classificação do produto e 144368 de registros sem o rótulo.\n",
    "\n",
    "São um total de 93 características na qual não há a descrição do que significa cada uma delas.  Sendo que não há dados faltando. O range dos dados vão de 0 a 352."
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   "source": [
    "# Configure to show multiples outputs from a single cell\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "import zipfile\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix\n",
    "import numpy as np\n",
    "import math\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.neural_network import MLPClassifier\n"
   ]
  },
  {
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       "     target  categories\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 93 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    feat_1  feat_2  feat_3  feat_4  feat_5  feat_6  feat_7  feat_8  feat_9  \\\n",
       "id                                                                           \n",
       "1        0       0       0       0       0       0       0       0       0   \n",
       "2        2       2      14      16       0       0       0       0       0   \n",
       "3        0       1      12       1       0       0       0       0       0   \n",
       "4        0       0       0       1       0       0       0       0       0   \n",
       "5        1       0       0       1       0       0       1       2       0   \n",
       "\n",
       "    feat_10   ...     feat_84  feat_85  feat_86  feat_87  feat_88  feat_89  \\\n",
       "id            ...                                                            \n",
       "1         3   ...           0        0       11        1       20        0   \n",
       "2         0   ...           0        0        0        0        0        4   \n",
       "3         0   ...           0        0        0        0        2        0   \n",
       "4         0   ...           0        3        1        0        0        0   \n",
       "5         3   ...           0        0        0        0        0        0   \n",
       "\n",
       "    feat_90  feat_91  feat_92  feat_93  \n",
       "id                                      \n",
       "1         0        0        0        0  \n",
       "2         0        0        2        0  \n",
       "3         0        0        0        1  \n",
       "4         0        0        0        0  \n",
       "5         0        9        0        0  \n",
       "\n",
       "[5 rows x 93 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with zipfile.ZipFile('Datasets.zip') as ziped_file:\n",
    "    with ziped_file.open('Datasets/train.csv') as train_file:\n",
    "        df_train = pd.read_csv(train_file, header=0).set_index('id')\n",
    "    with ziped_file.open('Datasets/test.csv') as test_file:\n",
    "        df_test = pd.read_csv(test_file, header=0).set_index('id')\n",
    "df_target = pd.DataFrame(df_train.pop('target')) # Get the target\n",
    "df_target.target = pd.Categorical(df_target.target) # Transform target in Categorical type\n",
    "df_target['categories'] = df_target.target.cat.codes # Add the codes in a columns\n",
    "df_target.head() # Show target classes\n",
    "df_train.head() # The train dataset\n",
    "df_test.head() # It hasn't target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmark\n",
    "\n",
    "A variável results é um acumulador para salvar os resultados na base de treino e teste de cada um dos modelos e compará-los ao final.\n",
    "\n",
    "Segue a estrutura:\n",
    "\n",
    "`\n",
    " 'modelo':\n",
    "     'teste': value\n",
    "     'treino': value\n",
    "`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "results = {}\n",
    "def add_results(model, train, test):\n",
    "    results[model] = {\n",
    "        'train': train*100,\n",
    "        'test': test*100,\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validation\n",
    "\n",
    "A abordagem para a Validação Cruzada é a utilização do método de k-partições. Neste método, o conjunto de dados é dividido em k partições [(WITTEN e FRANK, 2000)](ftp://ftp.ingv.it/pub/manuela.sbarra/Data%20Mining%20Practical%20Machine%20Learning%20Tools%20and%20Techniques%20-%20WEKA.pdf), testes extensivos em diversas bases de dados, utilizando diversos algoritmos, identificaram o valor de k para identificar a melhor margem de erro como sendo 10, também de forma randômica. Então, o conjunto de dados de treinamento é criado com k – 1 partições, e apenas uma partição é utilizada para testes. São realizadas k iterações, aonde cada partição é utilizada uma vez para testes enquanto as outras são utilizadas para treinamento. Após todas as partições terem sido utilizadas para teste, a margem de erro de cada iteração é somada e a média das k iterações se torna a margem de erro do modelo.\n",
    "\n",
    "![cross val](crossval.png) \n",
    "<center>Representação do método Cross Validation com k = 10.\n",
    "**Fonte**: BABATUNDE et al., 2015.</center>\n",
    "\n",
    "# Tratamento\n",
    "\n",
    "Será realizada as etapas de feature selection e feature engineering.  Correlação entre features Será realizada uma análise da correlação entre as features. Visto que há um total de 93 colunas que não foi disponibilizada nenhuma informação sobre o que são elas e o que representam e portanto, esta análize ajudará a identificar as relações entre as features.\n",
    "\n",
    "## Correlação\n",
    "\n",
    "\n",
    "A correlação entre duas variáveis é quando existe algum laço matemático que envolve o valor de duas variáveis de alguma forma [ESTATÍSTICA II - CORRELAÇÃO E REGRESSÃO](http://www.ctec.ufal.br/professor/mgn/05CorrelacaoERegressao.pdf).\n",
    "\n",
    "Uma das maneiras mais simples de se identificar a correlação entre duas variáveis é plotando-as em um gráfico, para tentar identificar alguma relação entre elas, entretanto, como são um total de 93 features, dificulta visualizar a correlação em forma gráfica.\n",
    "\n",
    "A correlação de [Pearson](https://pt.wikipedia.org/wiki/Coeficiente_de_correla%C3%A7%C3%A3o_de%0A_Pearson) mede o grau da correlação (e a direcção dessa correlação - se positiva ou negativa) entre duas variáveis de escala métrica (intervalar ou de rácio/razão).\n",
    "\n",
    "Já a correlação de [Spearman](https://pt.wikipedia.org/wiki/Coeficiente_de_correla%C3%A7%C3%A3o_de_postos_de_Spearman) entre duas variáveis é igual à correlação de Pearson entre os valores de postos daquelas duas variáveis. Enquanto a correlação de Pearson avalia relações lineares, a correlação de Spearman avalia relações monótonas, sejam elas lineares ou não.\n",
    "\n",
    "Visto ambos os tipos de correlação, utilizaremos a de Pearson para avaliar se há alguma correlação linear crescente ou decrescente entre as variáveis, pois esta relação nos possibilita remover uma delas sem prejuizos aos modelos de machine learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feat_1</th>\n",
       "      <th>feat_2</th>\n",
       "      <th>feat_3</th>\n",
       "      <th>feat_4</th>\n",
       "      <th>feat_5</th>\n",
       "      <th>feat_6</th>\n",
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       "      <th>feat_8</th>\n",
       "      <th>feat_9</th>\n",
       "      <th>feat_10</th>\n",
       "      <th>...</th>\n",
       "      <th>feat_84</th>\n",
       "      <th>feat_85</th>\n",
       "      <th>feat_86</th>\n",
       "      <th>feat_87</th>\n",
       "      <th>feat_88</th>\n",
       "      <th>feat_89</th>\n",
       "      <th>feat_90</th>\n",
       "      <th>feat_91</th>\n",
       "      <th>feat_92</th>\n",
       "      <th>feat_93</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>feat_1</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>feat_2</th>\n",
       "      <td>0.031332</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_3</th>\n",
       "      <td>0.027807</td>\n",
       "      <td>0.082573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_4</th>\n",
       "      <td>0.027529</td>\n",
       "      <td>0.134987</td>\n",
       "      <td>0.583523</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_5</th>\n",
       "      <td>0.042973</td>\n",
       "      <td>0.020926</td>\n",
       "      <td>0.010880</td>\n",
       "      <td>1.729026e-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_6</th>\n",
       "      <td>0.043603</td>\n",
       "      <td>0.041343</td>\n",
       "      <td>0.004288</td>\n",
       "      <td>1.405895e-02</td>\n",
       "      <td>0.145355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_7</th>\n",
       "      <td>0.298952</td>\n",
       "      <td>0.222386</td>\n",
       "      <td>0.001294</td>\n",
       "      <td>1.448981e-02</td>\n",
       "      <td>0.075047</td>\n",
       "      <td>0.088014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_8</th>\n",
       "      <td>0.056321</td>\n",
       "      <td>0.019815</td>\n",
       "      <td>0.053462</td>\n",
       "      <td>4.618407e-02</td>\n",
       "      <td>0.035861</td>\n",
       "      <td>0.012867</td>\n",
       "      <td>0.038121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_9</th>\n",
       "      <td>0.032285</td>\n",
       "      <td>0.025630</td>\n",
       "      <td>0.063551</td>\n",
       "      <td>4.624977e-02</td>\n",
       "      <td>0.024708</td>\n",
       "      <td>0.009373</td>\n",
       "      <td>0.027146</td>\n",
       "      <td>0.039281</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_10</th>\n",
       "      <td>0.097776</td>\n",
       "      <td>0.051925</td>\n",
       "      <td>0.036944</td>\n",
       "      <td>5.951396e-02</td>\n",
       "      <td>0.091324</td>\n",
       "      <td>0.041940</td>\n",
       "      <td>0.194258</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.024323</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_11</th>\n",
       "      <td>0.042928</td>\n",
       "      <td>0.118534</td>\n",
       "      <td>0.596243</td>\n",
       "      <td>3.894092e-01</td>\n",
       "      <td>0.004882</td>\n",
       "      <td>0.014504</td>\n",
       "      <td>0.012418</td>\n",
       "      <td>0.065923</td>\n",
       "      <td>0.075820</td>\n",
       "      <td>0.006010</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_12</th>\n",
       "      <td>0.056934</td>\n",
       "      <td>0.090153</td>\n",
       "      <td>0.050037</td>\n",
       "      <td>5.743356e-02</td>\n",
       "      <td>0.036668</td>\n",
       "      <td>0.028588</td>\n",
       "      <td>0.056230</td>\n",
       "      <td>0.091424</td>\n",
       "      <td>0.021885</td>\n",
       "      <td>0.048969</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_13</th>\n",
       "      <td>0.139254</td>\n",
       "      <td>0.157467</td>\n",
       "      <td>0.013870</td>\n",
       "      <td>2.897317e-02</td>\n",
       "      <td>0.059081</td>\n",
       "      <td>0.036293</td>\n",
       "      <td>0.199142</td>\n",
       "      <td>0.095365</td>\n",
       "      <td>0.040164</td>\n",
       "      <td>0.086682</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_14</th>\n",
       "      <td>0.063517</td>\n",
       "      <td>0.070057</td>\n",
       "      <td>0.111105</td>\n",
       "      <td>9.921490e-02</td>\n",
       "      <td>0.037607</td>\n",
       "      <td>0.027350</td>\n",
       "      <td>0.044671</td>\n",
       "      <td>0.061799</td>\n",
       "      <td>0.110188</td>\n",
       "      <td>0.029598</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_15</th>\n",
       "      <td>0.045738</td>\n",
       "      <td>0.048798</td>\n",
       "      <td>0.065285</td>\n",
       "      <td>5.122155e-02</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.018328</td>\n",
       "      <td>0.035721</td>\n",
       "      <td>0.056960</td>\n",
       "      <td>0.009858</td>\n",
       "      <td>0.021700</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_16</th>\n",
       "      <td>0.027086</td>\n",
       "      <td>0.108046</td>\n",
       "      <td>0.221426</td>\n",
       "      <td>2.110780e-01</td>\n",
       "      <td>0.062877</td>\n",
       "      <td>0.021934</td>\n",
       "      <td>0.043957</td>\n",
       "      <td>0.004659</td>\n",
       "      <td>0.082664</td>\n",
       "      <td>0.063997</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_17</th>\n",
       "      <td>0.053004</td>\n",
       "      <td>0.074902</td>\n",
       "      <td>0.023093</td>\n",
       "      <td>7.553867e-03</td>\n",
       "      <td>0.062197</td>\n",
       "      <td>0.015488</td>\n",
       "      <td>0.127245</td>\n",
       "      <td>0.173912</td>\n",
       "      <td>0.028709</td>\n",
       "      <td>0.092959</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_18</th>\n",
       "      <td>0.084856</td>\n",
       "      <td>0.242716</td>\n",
       "      <td>0.115655</td>\n",
       "      <td>2.148952e-01</td>\n",
       "      <td>0.052186</td>\n",
       "      <td>0.048710</td>\n",
       "      <td>0.098972</td>\n",
       "      <td>0.087777</td>\n",
       "      <td>0.043642</td>\n",
       "      <td>0.071635</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_19</th>\n",
       "      <td>0.002302</td>\n",
       "      <td>0.176655</td>\n",
       "      <td>0.012228</td>\n",
       "      <td>3.519107e-07</td>\n",
       "      <td>0.008556</td>\n",
       "      <td>0.038493</td>\n",
       "      <td>0.058071</td>\n",
       "      <td>0.019387</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.009015</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_20</th>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.449160</td>\n",
       "      <td>0.011069</td>\n",
       "      <td>4.465657e-02</td>\n",
       "      <td>0.046200</td>\n",
       "      <td>0.057813</td>\n",
       "      <td>0.364972</td>\n",
       "      <td>0.062595</td>\n",
       "      <td>0.023397</td>\n",
       "      <td>0.176373</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_21</th>\n",
       "      <td>0.027026</td>\n",
       "      <td>0.014113</td>\n",
       "      <td>0.354925</td>\n",
       "      <td>2.329227e-01</td>\n",
       "      <td>0.003288</td>\n",
       "      <td>0.008046</td>\n",
       "      <td>0.022908</td>\n",
       "      <td>0.041095</td>\n",
       "      <td>0.028409</td>\n",
       "      <td>0.005134</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_22</th>\n",
       "      <td>0.063283</td>\n",
       "      <td>0.215106</td>\n",
       "      <td>0.251082</td>\n",
       "      <td>2.477378e-01</td>\n",
       "      <td>0.075161</td>\n",
       "      <td>0.038939</td>\n",
       "      <td>0.162620</td>\n",
       "      <td>0.029032</td>\n",
       "      <td>0.062348</td>\n",
       "      <td>0.141405</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_23</th>\n",
       "      <td>0.048686</td>\n",
       "      <td>0.162065</td>\n",
       "      <td>0.002427</td>\n",
       "      <td>3.062225e-02</td>\n",
       "      <td>0.017281</td>\n",
       "      <td>0.043651</td>\n",
       "      <td>0.186462</td>\n",
       "      <td>0.012774</td>\n",
       "      <td>0.006940</td>\n",
       "      <td>0.096666</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_24</th>\n",
       "      <td>0.067255</td>\n",
       "      <td>0.253684</td>\n",
       "      <td>0.031596</td>\n",
       "      <td>3.727726e-03</td>\n",
       "      <td>0.075222</td>\n",
       "      <td>0.082124</td>\n",
       "      <td>0.244813</td>\n",
       "      <td>0.161848</td>\n",
       "      <td>0.073618</td>\n",
       "      <td>0.081684</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_25</th>\n",
       "      <td>0.187237</td>\n",
       "      <td>0.096366</td>\n",
       "      <td>0.157459</td>\n",
       "      <td>1.342306e-01</td>\n",
       "      <td>0.003610</td>\n",
       "      <td>0.023319</td>\n",
       "      <td>0.048820</td>\n",
       "      <td>0.036939</td>\n",
       "      <td>0.025279</td>\n",
       "      <td>0.009792</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_26</th>\n",
       "      <td>0.022813</td>\n",
       "      <td>0.064856</td>\n",
       "      <td>0.268112</td>\n",
       "      <td>3.657567e-01</td>\n",
       "      <td>0.025116</td>\n",
       "      <td>0.004680</td>\n",
       "      <td>0.008782</td>\n",
       "      <td>0.041599</td>\n",
       "      <td>0.066414</td>\n",
       "      <td>0.003721</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_27</th>\n",
       "      <td>0.038826</td>\n",
       "      <td>0.037841</td>\n",
       "      <td>0.508370</td>\n",
       "      <td>3.086287e-01</td>\n",
       "      <td>0.002098</td>\n",
       "      <td>0.001943</td>\n",
       "      <td>0.015429</td>\n",
       "      <td>0.050272</td>\n",
       "      <td>0.042531</td>\n",
       "      <td>0.001551</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_28</th>\n",
       "      <td>0.030257</td>\n",
       "      <td>0.072494</td>\n",
       "      <td>0.551398</td>\n",
       "      <td>4.864171e-01</td>\n",
       "      <td>0.047688</td>\n",
       "      <td>0.017132</td>\n",
       "      <td>0.000998</td>\n",
       "      <td>0.036668</td>\n",
       "      <td>0.055545</td>\n",
       "      <td>0.022349</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_29</th>\n",
       "      <td>0.069266</td>\n",
       "      <td>0.025689</td>\n",
       "      <td>0.004141</td>\n",
       "      <td>1.427066e-02</td>\n",
       "      <td>0.065957</td>\n",
       "      <td>0.002389</td>\n",
       "      <td>0.046231</td>\n",
       "      <td>0.104985</td>\n",
       "      <td>0.021328</td>\n",
       "      <td>0.068243</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_30</th>\n",
       "      <td>0.033108</td>\n",
       "      <td>0.026896</td>\n",
       "      <td>0.007667</td>\n",
       "      <td>8.733991e-04</td>\n",
       "      <td>0.318117</td>\n",
       "      <td>0.196493</td>\n",
       "      <td>0.050535</td>\n",
       "      <td>0.009574</td>\n",
       "      <td>0.015830</td>\n",
       "      <td>0.012623</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_64</th>\n",
       "      <td>0.010499</td>\n",
       "      <td>0.005354</td>\n",
       "      <td>0.065105</td>\n",
       "      <td>4.728813e-02</td>\n",
       "      <td>0.021017</td>\n",
       "      <td>0.002764</td>\n",
       "      <td>0.011165</td>\n",
       "      <td>0.003194</td>\n",
       "      <td>0.702951</td>\n",
       "      <td>0.022536</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_65</th>\n",
       "      <td>0.110041</td>\n",
       "      <td>0.078801</td>\n",
       "      <td>0.065492</td>\n",
       "      <td>6.228472e-02</td>\n",
       "      <td>0.228349</td>\n",
       "      <td>0.066867</td>\n",
       "      <td>0.202346</td>\n",
       "      <td>0.025544</td>\n",
       "      <td>0.038163</td>\n",
       "      <td>0.182756</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_66</th>\n",
       "      <td>0.053010</td>\n",
       "      <td>0.175620</td>\n",
       "      <td>0.088017</td>\n",
       "      <td>1.296545e-01</td>\n",
       "      <td>0.048364</td>\n",
       "      <td>0.033285</td>\n",
       "      <td>0.122660</td>\n",
       "      <td>0.115175</td>\n",
       "      <td>0.001778</td>\n",
       "      <td>0.100722</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_67</th>\n",
       "      <td>0.154301</td>\n",
       "      <td>0.068667</td>\n",
       "      <td>0.110081</td>\n",
       "      <td>8.045694e-02</td>\n",
       "      <td>0.061964</td>\n",
       "      <td>0.038289</td>\n",
       "      <td>0.148598</td>\n",
       "      <td>0.320949</td>\n",
       "      <td>0.176921</td>\n",
       "      <td>0.043117</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_68</th>\n",
       "      <td>0.014674</td>\n",
       "      <td>0.012802</td>\n",
       "      <td>0.030992</td>\n",
       "      <td>2.009191e-02</td>\n",
       "      <td>0.107405</td>\n",
       "      <td>0.021619</td>\n",
       "      <td>0.040309</td>\n",
       "      <td>0.075384</td>\n",
       "      <td>0.012192</td>\n",
       "      <td>0.001693</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_69</th>\n",
       "      <td>0.007544</td>\n",
       "      <td>0.307406</td>\n",
       "      <td>0.032748</td>\n",
       "      <td>1.446082e-02</td>\n",
       "      <td>0.003294</td>\n",
       "      <td>0.074836</td>\n",
       "      <td>0.131430</td>\n",
       "      <td>0.046258</td>\n",
       "      <td>0.029335</td>\n",
       "      <td>0.077354</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_70</th>\n",
       "      <td>0.165442</td>\n",
       "      <td>0.112968</td>\n",
       "      <td>0.018774</td>\n",
       "      <td>2.079779e-02</td>\n",
       "      <td>0.118510</td>\n",
       "      <td>0.052401</td>\n",
       "      <td>0.237907</td>\n",
       "      <td>0.023089</td>\n",
       "      <td>0.056205</td>\n",
       "      <td>0.322857</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_71</th>\n",
       "      <td>0.013712</td>\n",
       "      <td>0.002336</td>\n",
       "      <td>0.053020</td>\n",
       "      <td>4.241268e-02</td>\n",
       "      <td>0.056428</td>\n",
       "      <td>0.011901</td>\n",
       "      <td>0.115813</td>\n",
       "      <td>0.081664</td>\n",
       "      <td>0.043286</td>\n",
       "      <td>0.104834</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_72</th>\n",
       "      <td>0.029983</td>\n",
       "      <td>0.023267</td>\n",
       "      <td>0.045339</td>\n",
       "      <td>2.979578e-02</td>\n",
       "      <td>0.005177</td>\n",
       "      <td>0.011090</td>\n",
       "      <td>0.014921</td>\n",
       "      <td>0.029868</td>\n",
       "      <td>0.058147</td>\n",
       "      <td>0.004225</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_73</th>\n",
       "      <td>0.140815</td>\n",
       "      <td>0.039192</td>\n",
       "      <td>0.013972</td>\n",
       "      <td>1.128547e-02</td>\n",
       "      <td>0.001609</td>\n",
       "      <td>0.025023</td>\n",
       "      <td>0.022819</td>\n",
       "      <td>0.028999</td>\n",
       "      <td>0.022679</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_74</th>\n",
       "      <td>0.051365</td>\n",
       "      <td>0.070724</td>\n",
       "      <td>0.041559</td>\n",
       "      <td>4.909735e-02</td>\n",
       "      <td>0.017265</td>\n",
       "      <td>0.043160</td>\n",
       "      <td>0.053059</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.007594</td>\n",
       "      <td>0.008912</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_75</th>\n",
       "      <td>0.011596</td>\n",
       "      <td>0.093689</td>\n",
       "      <td>0.044724</td>\n",
       "      <td>3.145389e-02</td>\n",
       "      <td>0.015279</td>\n",
       "      <td>0.006951</td>\n",
       "      <td>0.039865</td>\n",
       "      <td>0.031466</td>\n",
       "      <td>0.027313</td>\n",
       "      <td>0.003828</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_76</th>\n",
       "      <td>0.153808</td>\n",
       "      <td>0.259360</td>\n",
       "      <td>0.028670</td>\n",
       "      <td>1.379188e-02</td>\n",
       "      <td>0.035570</td>\n",
       "      <td>0.073867</td>\n",
       "      <td>0.375114</td>\n",
       "      <td>0.081682</td>\n",
       "      <td>0.027424</td>\n",
       "      <td>0.106752</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_77</th>\n",
       "      <td>0.123752</td>\n",
       "      <td>0.014911</td>\n",
       "      <td>0.001584</td>\n",
       "      <td>1.531773e-02</td>\n",
       "      <td>0.030462</td>\n",
       "      <td>0.006501</td>\n",
       "      <td>0.005769</td>\n",
       "      <td>0.027486</td>\n",
       "      <td>0.020185</td>\n",
       "      <td>0.019069</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_78</th>\n",
       "      <td>0.279202</td>\n",
       "      <td>0.094256</td>\n",
       "      <td>0.021979</td>\n",
       "      <td>1.449856e-02</td>\n",
       "      <td>0.070709</td>\n",
       "      <td>0.061250</td>\n",
       "      <td>0.567084</td>\n",
       "      <td>0.079623</td>\n",
       "      <td>0.015922</td>\n",
       "      <td>0.091760</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_79</th>\n",
       "      <td>0.228912</td>\n",
       "      <td>0.033668</td>\n",
       "      <td>0.020566</td>\n",
       "      <td>1.083473e-02</td>\n",
       "      <td>0.055115</td>\n",
       "      <td>0.009942</td>\n",
       "      <td>0.066753</td>\n",
       "      <td>0.083714</td>\n",
       "      <td>0.036116</td>\n",
       "      <td>0.113659</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_80</th>\n",
       "      <td>0.013303</td>\n",
       "      <td>0.155768</td>\n",
       "      <td>0.442036</td>\n",
       "      <td>4.057725e-01</td>\n",
       "      <td>0.026223</td>\n",
       "      <td>0.017648</td>\n",
       "      <td>0.028860</td>\n",
       "      <td>0.038382</td>\n",
       "      <td>0.046721</td>\n",
       "      <td>0.019042</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_81</th>\n",
       "      <td>0.032427</td>\n",
       "      <td>0.052101</td>\n",
       "      <td>0.013089</td>\n",
       "      <td>2.828377e-02</td>\n",
       "      <td>0.129333</td>\n",
       "      <td>0.044136</td>\n",
       "      <td>0.144308</td>\n",
       "      <td>0.035102</td>\n",
       "      <td>0.005847</td>\n",
       "      <td>0.135928</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_82</th>\n",
       "      <td>0.026085</td>\n",
       "      <td>0.119109</td>\n",
       "      <td>0.438458</td>\n",
       "      <td>4.365413e-01</td>\n",
       "      <td>0.057400</td>\n",
       "      <td>0.014907</td>\n",
       "      <td>0.022059</td>\n",
       "      <td>0.034409</td>\n",
       "      <td>0.039806</td>\n",
       "      <td>0.029741</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_83</th>\n",
       "      <td>0.059165</td>\n",
       "      <td>0.371691</td>\n",
       "      <td>0.019914</td>\n",
       "      <td>1.051874e-03</td>\n",
       "      <td>0.008006</td>\n",
       "      <td>0.035145</td>\n",
       "      <td>0.282069</td>\n",
       "      <td>0.033479</td>\n",
       "      <td>0.032875</td>\n",
       "      <td>0.052025</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_84</th>\n",
       "      <td>0.049634</td>\n",
       "      <td>0.009845</td>\n",
       "      <td>0.011159</td>\n",
       "      <td>5.684499e-03</td>\n",
       "      <td>0.467329</td>\n",
       "      <td>0.177777</td>\n",
       "      <td>0.062634</td>\n",
       "      <td>0.005064</td>\n",
       "      <td>0.013569</td>\n",
       "      <td>0.017939</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_85</th>\n",
       "      <td>0.008739</td>\n",
       "      <td>0.006764</td>\n",
       "      <td>0.048626</td>\n",
       "      <td>3.315343e-02</td>\n",
       "      <td>0.034062</td>\n",
       "      <td>0.004290</td>\n",
       "      <td>0.037874</td>\n",
       "      <td>0.003416</td>\n",
       "      <td>0.031462</td>\n",
       "      <td>0.086758</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_86</th>\n",
       "      <td>0.107947</td>\n",
       "      <td>0.039090</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>7.102916e-02</td>\n",
       "      <td>0.013879</td>\n",
       "      <td>0.010455</td>\n",
       "      <td>0.009169</td>\n",
       "      <td>0.029395</td>\n",
       "      <td>0.019144</td>\n",
       "      <td>0.159447</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003459</td>\n",
       "      <td>0.109643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_87</th>\n",
       "      <td>0.089374</td>\n",
       "      <td>0.047451</td>\n",
       "      <td>0.009838</td>\n",
       "      <td>5.054728e-03</td>\n",
       "      <td>0.013999</td>\n",
       "      <td>0.015256</td>\n",
       "      <td>0.089574</td>\n",
       "      <td>0.059929</td>\n",
       "      <td>0.016925</td>\n",
       "      <td>0.077421</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013631</td>\n",
       "      <td>0.049250</td>\n",
       "      <td>0.073685</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_88</th>\n",
       "      <td>0.020830</td>\n",
       "      <td>0.047035</td>\n",
       "      <td>0.082336</td>\n",
       "      <td>6.748367e-02</td>\n",
       "      <td>0.019201</td>\n",
       "      <td>0.015437</td>\n",
       "      <td>0.033646</td>\n",
       "      <td>0.050931</td>\n",
       "      <td>0.001160</td>\n",
       "      <td>0.054635</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017903</td>\n",
       "      <td>0.027886</td>\n",
       "      <td>0.426972</td>\n",
       "      <td>0.023053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_89</th>\n",
       "      <td>0.096851</td>\n",
       "      <td>0.105527</td>\n",
       "      <td>0.174781</td>\n",
       "      <td>1.837145e-01</td>\n",
       "      <td>0.119951</td>\n",
       "      <td>0.035042</td>\n",
       "      <td>0.063511</td>\n",
       "      <td>0.007974</td>\n",
       "      <td>0.019147</td>\n",
       "      <td>0.061498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103643</td>\n",
       "      <td>0.053582</td>\n",
       "      <td>0.011822</td>\n",
       "      <td>0.066008</td>\n",
       "      <td>0.022552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_90</th>\n",
       "      <td>0.010310</td>\n",
       "      <td>0.515022</td>\n",
       "      <td>0.015068</td>\n",
       "      <td>9.454061e-03</td>\n",
       "      <td>0.004842</td>\n",
       "      <td>0.054034</td>\n",
       "      <td>0.129578</td>\n",
       "      <td>0.026807</td>\n",
       "      <td>0.020698</td>\n",
       "      <td>0.049908</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006013</td>\n",
       "      <td>0.003931</td>\n",
       "      <td>0.019803</td>\n",
       "      <td>0.014696</td>\n",
       "      <td>0.031679</td>\n",
       "      <td>0.027764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_91</th>\n",
       "      <td>0.037264</td>\n",
       "      <td>0.026383</td>\n",
       "      <td>0.012417</td>\n",
       "      <td>1.031241e-02</td>\n",
       "      <td>0.012012</td>\n",
       "      <td>0.012465</td>\n",
       "      <td>0.068506</td>\n",
       "      <td>0.095990</td>\n",
       "      <td>0.014742</td>\n",
       "      <td>0.024025</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003444</td>\n",
       "      <td>0.023091</td>\n",
       "      <td>0.024005</td>\n",
       "      <td>0.028850</td>\n",
       "      <td>0.033653</td>\n",
       "      <td>0.015917</td>\n",
       "      <td>0.014812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_92</th>\n",
       "      <td>0.054777</td>\n",
       "      <td>0.008219</td>\n",
       "      <td>0.066921</td>\n",
       "      <td>8.763105e-02</td>\n",
       "      <td>0.065331</td>\n",
       "      <td>0.015479</td>\n",
       "      <td>0.032261</td>\n",
       "      <td>0.013608</td>\n",
       "      <td>0.069707</td>\n",
       "      <td>0.006869</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048431</td>\n",
       "      <td>0.043484</td>\n",
       "      <td>0.049393</td>\n",
       "      <td>0.001424</td>\n",
       "      <td>0.070120</td>\n",
       "      <td>0.129622</td>\n",
       "      <td>0.035311</td>\n",
       "      <td>0.104226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feat_93</th>\n",
       "      <td>0.081783</td>\n",
       "      <td>0.054593</td>\n",
       "      <td>0.006814</td>\n",
       "      <td>1.574563e-02</td>\n",
       "      <td>0.002038</td>\n",
       "      <td>0.008521</td>\n",
       "      <td>0.034912</td>\n",
       "      <td>0.005131</td>\n",
       "      <td>0.006038</td>\n",
       "      <td>0.041316</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003723</td>\n",
       "      <td>0.023390</td>\n",
       "      <td>0.029035</td>\n",
       "      <td>0.499990</td>\n",
       "      <td>0.008631</td>\n",
       "      <td>0.030650</td>\n",
       "      <td>0.039864</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.003653</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 93 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           feat_1    feat_2    feat_3        feat_4    feat_5    feat_6  \\\n",
       "feat_1        NaN       NaN       NaN           NaN       NaN       NaN   \n",
       "feat_2   0.031332       NaN       NaN           NaN       NaN       NaN   \n",
       "feat_3   0.027807  0.082573       NaN           NaN       NaN       NaN   \n",
       "feat_4   0.027529  0.134987  0.583523           NaN       NaN       NaN   \n",
       "feat_5   0.042973  0.020926  0.010880  1.729026e-02       NaN       NaN   \n",
       "feat_6   0.043603  0.041343  0.004288  1.405895e-02  0.145355       NaN   \n",
       "feat_7   0.298952  0.222386  0.001294  1.448981e-02  0.075047  0.088014   \n",
       "feat_8   0.056321  0.019815  0.053462  4.618407e-02  0.035861  0.012867   \n",
       "feat_9   0.032285  0.025630  0.063551  4.624977e-02  0.024708  0.009373   \n",
       "feat_10  0.097776  0.051925  0.036944  5.951396e-02  0.091324  0.041940   \n",
       "feat_11  0.042928  0.118534  0.596243  3.894092e-01  0.004882  0.014504   \n",
       "feat_12  0.056934  0.090153  0.050037  5.743356e-02  0.036668  0.028588   \n",
       "feat_13  0.139254  0.157467  0.013870  2.897317e-02  0.059081  0.036293   \n",
       "feat_14  0.063517  0.070057  0.111105  9.921490e-02  0.037607  0.027350   \n",
       "feat_15  0.045738  0.048798  0.065285  5.122155e-02  0.007000  0.018328   \n",
       "feat_16  0.027086  0.108046  0.221426  2.110780e-01  0.062877  0.021934   \n",
       "feat_17  0.053004  0.074902  0.023093  7.553867e-03  0.062197  0.015488   \n",
       "feat_18  0.084856  0.242716  0.115655  2.148952e-01  0.052186  0.048710   \n",
       "feat_19  0.002302  0.176655  0.012228  3.519107e-07  0.008556  0.038493   \n",
       "feat_20  0.070511  0.449160  0.011069  4.465657e-02  0.046200  0.057813   \n",
       "feat_21  0.027026  0.014113  0.354925  2.329227e-01  0.003288  0.008046   \n",
       "feat_22  0.063283  0.215106  0.251082  2.477378e-01  0.075161  0.038939   \n",
       "feat_23  0.048686  0.162065  0.002427  3.062225e-02  0.017281  0.043651   \n",
       "feat_24  0.067255  0.253684  0.031596  3.727726e-03  0.075222  0.082124   \n",
       "feat_25  0.187237  0.096366  0.157459  1.342306e-01  0.003610  0.023319   \n",
       "feat_26  0.022813  0.064856  0.268112  3.657567e-01  0.025116  0.004680   \n",
       "feat_27  0.038826  0.037841  0.508370  3.086287e-01  0.002098  0.001943   \n",
       "feat_28  0.030257  0.072494  0.551398  4.864171e-01  0.047688  0.017132   \n",
       "feat_29  0.069266  0.025689  0.004141  1.427066e-02  0.065957  0.002389   \n",
       "feat_30  0.033108  0.026896  0.007667  8.733991e-04  0.318117  0.196493   \n",
       "...           ...       ...       ...           ...       ...       ...   \n",
       "feat_64  0.010499  0.005354  0.065105  4.728813e-02  0.021017  0.002764   \n",
       "feat_65  0.110041  0.078801  0.065492  6.228472e-02  0.228349  0.066867   \n",
       "feat_66  0.053010  0.175620  0.088017  1.296545e-01  0.048364  0.033285   \n",
       "feat_67  0.154301  0.068667  0.110081  8.045694e-02  0.061964  0.038289   \n",
       "feat_68  0.014674  0.012802  0.030992  2.009191e-02  0.107405  0.021619   \n",
       "feat_69  0.007544  0.307406  0.032748  1.446082e-02  0.003294  0.074836   \n",
       "feat_70  0.165442  0.112968  0.018774  2.079779e-02  0.118510  0.052401   \n",
       "feat_71  0.013712  0.002336  0.053020  4.241268e-02  0.056428  0.011901   \n",
       "feat_72  0.029983  0.023267  0.045339  2.979578e-02  0.005177  0.011090   \n",
       "feat_73  0.140815  0.039192  0.013972  1.128547e-02  0.001609  0.025023   \n",
       "feat_74  0.051365  0.070724  0.041559  4.909735e-02  0.017265  0.043160   \n",
       "feat_75  0.011596  0.093689  0.044724  3.145389e-02  0.015279  0.006951   \n",
       "feat_76  0.153808  0.259360  0.028670  1.379188e-02  0.035570  0.073867   \n",
       "feat_77  0.123752  0.014911  0.001584  1.531773e-02  0.030462  0.006501   \n",
       "feat_78  0.279202  0.094256  0.021979  1.449856e-02  0.070709  0.061250   \n",
       "feat_79  0.228912  0.033668  0.020566  1.083473e-02  0.055115  0.009942   \n",
       "feat_80  0.013303  0.155768  0.442036  4.057725e-01  0.026223  0.017648   \n",
       "feat_81  0.032427  0.052101  0.013089  2.828377e-02  0.129333  0.044136   \n",
       "feat_82  0.026085  0.119109  0.438458  4.365413e-01  0.057400  0.014907   \n",
       "feat_83  0.059165  0.371691  0.019914  1.051874e-03  0.008006  0.035145   \n",
       "feat_84  0.049634  0.009845  0.011159  5.684499e-03  0.467329  0.177777   \n",
       "feat_85  0.008739  0.006764  0.048626  3.315343e-02  0.034062  0.004290   \n",
       "feat_86  0.107947  0.039090  0.096093  7.102916e-02  0.013879  0.010455   \n",
       "feat_87  0.089374  0.047451  0.009838  5.054728e-03  0.013999  0.015256   \n",
       "feat_88  0.020830  0.047035  0.082336  6.748367e-02  0.019201  0.015437   \n",
       "feat_89  0.096851  0.105527  0.174781  1.837145e-01  0.119951  0.035042   \n",
       "feat_90  0.010310  0.515022  0.015068  9.454061e-03  0.004842  0.054034   \n",
       "feat_91  0.037264  0.026383  0.012417  1.031241e-02  0.012012  0.012465   \n",
       "feat_92  0.054777  0.008219  0.066921  8.763105e-02  0.065331  0.015479   \n",
       "feat_93  0.081783  0.054593  0.006814  1.574563e-02  0.002038  0.008521   \n",
       "\n",
       "           feat_7    feat_8    feat_9   feat_10   ...      feat_84   feat_85  \\\n",
       "feat_1        NaN       NaN       NaN       NaN   ...          NaN       NaN   \n",
       "feat_2        NaN       NaN       NaN       NaN   ...          NaN       NaN   \n",
       "feat_3        NaN       NaN       NaN       NaN   ...          NaN       NaN   \n",
       "feat_4        NaN       NaN       NaN       NaN   ...          NaN       NaN   \n",
       "feat_5        NaN       NaN       NaN       NaN   ...          NaN       NaN   \n",
       "feat_6        NaN       NaN       NaN       NaN   ...          NaN       NaN   \n",
       "feat_7        NaN       NaN       NaN       NaN   ...          NaN       NaN   \n",
       "feat_8   0.038121       NaN       NaN       NaN   ...          NaN       NaN   \n",
       "feat_9   0.027146  0.039281       NaN       NaN   ...          NaN       NaN   \n",
       "feat_10  0.194258  0.000023  0.024323       NaN   ...          NaN       NaN   \n",
       "feat_11  0.012418  0.065923  0.075820  0.006010   ...          NaN       NaN   \n",
       "feat_12  0.056230  0.091424  0.021885  0.048969   ...          NaN       NaN   \n",
       "feat_13  0.199142  0.095365  0.040164  0.086682   ...          NaN       NaN   \n",
       "feat_14  0.044671  0.061799  0.110188  0.029598   ...          NaN       NaN   \n",
       "feat_15  0.035721  0.056960  0.009858  0.021700   ...          NaN       NaN   \n",
       "feat_16  0.043957  0.004659  0.082664  0.063997   ...          NaN       NaN   \n",
       "feat_17  0.127245  0.173912  0.028709  0.092959   ...          NaN       NaN   \n",
       "feat_18  0.098972  0.087777  0.043642  0.071635   ...          NaN       NaN   \n",
       "feat_19  0.058071  0.019387  0.000167  0.009015   ...          NaN       NaN   \n",
       "feat_20  0.364972  0.062595  0.023397  0.176373   ...          NaN       NaN   \n",
       "feat_21  0.022908  0.041095  0.028409  0.005134   ...          NaN       NaN   \n",
       "feat_22  0.162620  0.029032  0.062348  0.141405   ...          NaN       NaN   \n",
       "feat_23  0.186462  0.012774  0.006940  0.096666   ...          NaN       NaN   \n",
       "feat_24  0.244813  0.161848  0.073618  0.081684   ...          NaN       NaN   \n",
       "feat_25  0.048820  0.036939  0.025279  0.009792   ...          NaN       NaN   \n",
       "feat_26  0.008782  0.041599  0.066414  0.003721   ...          NaN       NaN   \n",
       "feat_27  0.015429  0.050272  0.042531  0.001551   ...          NaN       NaN   \n",
       "feat_28  0.000998  0.036668  0.055545  0.022349   ...          NaN       NaN   \n",
       "feat_29  0.046231  0.104985  0.021328  0.068243   ...          NaN       NaN   \n",
       "feat_30  0.050535  0.009574  0.015830  0.012623   ...          NaN       NaN   \n",
       "...           ...       ...       ...       ...   ...          ...       ...   \n",
       "feat_64  0.011165  0.003194  0.702951  0.022536   ...          NaN       NaN   \n",
       "feat_65  0.202346  0.025544  0.038163  0.182756   ...          NaN       NaN   \n",
       "feat_66  0.122660  0.115175  0.001778  0.100722   ...          NaN       NaN   \n",
       "feat_67  0.148598  0.320949  0.176921  0.043117   ...          NaN       NaN   \n",
       "feat_68  0.040309  0.075384  0.012192  0.001693   ...          NaN       NaN   \n",
       "feat_69  0.131430  0.046258  0.029335  0.077354   ...          NaN       NaN   \n",
       "feat_70  0.237907  0.023089  0.056205  0.322857   ...          NaN       NaN   \n",
       "feat_71  0.115813  0.081664  0.043286  0.104834   ...          NaN       NaN   \n",
       "feat_72  0.014921  0.029868  0.058147  0.004225   ...          NaN       NaN   \n",
       "feat_73  0.022819  0.028999  0.022679  0.000240   ...          NaN       NaN   \n",
       "feat_74  0.053059  0.000431  0.007594  0.008912   ...          NaN       NaN   \n",
       "feat_75  0.039865  0.031466  0.027313  0.003828   ...          NaN       NaN   \n",
       "feat_76  0.375114  0.081682  0.027424  0.106752   ...          NaN       NaN   \n",
       "feat_77  0.005769  0.027486  0.020185  0.019069   ...          NaN       NaN   \n",
       "feat_78  0.567084  0.079623  0.015922  0.091760   ...          NaN       NaN   \n",
       "feat_79  0.066753  0.083714  0.036116  0.113659   ...          NaN       NaN   \n",
       "feat_80  0.028860  0.038382  0.046721  0.019042   ...          NaN       NaN   \n",
       "feat_81  0.144308  0.035102  0.005847  0.135928   ...          NaN       NaN   \n",
       "feat_82  0.022059  0.034409  0.039806  0.029741   ...          NaN       NaN   \n",
       "feat_83  0.282069  0.033479  0.032875  0.052025   ...          NaN       NaN   \n",
       "feat_84  0.062634  0.005064  0.013569  0.017939   ...          NaN       NaN   \n",
       "feat_85  0.037874  0.003416  0.031462  0.086758   ...     0.010210       NaN   \n",
       "feat_86  0.009169  0.029395  0.019144  0.159447   ...     0.003459  0.109643   \n",
       "feat_87  0.089574  0.059929  0.016925  0.077421   ...     0.013631  0.049250   \n",
       "feat_88  0.033646  0.050931  0.001160  0.054635   ...     0.017903  0.027886   \n",
       "feat_89  0.063511  0.007974  0.019147  0.061498   ...     0.103643  0.053582   \n",
       "feat_90  0.129578  0.026807  0.020698  0.049908   ...     0.006013  0.003931   \n",
       "feat_91  0.068506  0.095990  0.014742  0.024025   ...     0.003444  0.023091   \n",
       "feat_92  0.032261  0.013608  0.069707  0.006869   ...     0.048431  0.043484   \n",
       "feat_93  0.034912  0.005131  0.006038  0.041316   ...     0.003723  0.023390   \n",
       "\n",
       "          feat_86   feat_87   feat_88   feat_89   feat_90   feat_91   feat_92  \\\n",
       "feat_1        NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_2        NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_3        NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_4        NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_5        NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_6        NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_7        NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_8        NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_9        NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_10       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_11       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_12       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_13       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_14       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_15       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_16       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_17       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_18       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_19       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_20       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_21       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_22       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_23       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_24       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_25       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_26       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_27       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_28       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_29       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_30       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "...           ...       ...       ...       ...       ...       ...       ...   \n",
       "feat_64       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_65       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_66       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_67       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_68       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_69       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_70       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_71       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_72       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_73       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_74       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_75       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_76       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_77       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_78       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_79       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_80       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_81       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_82       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_83       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_84       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_85       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_86       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_87  0.073685       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_88  0.426972  0.023053       NaN       NaN       NaN       NaN       NaN   \n",
       "feat_89  0.011822  0.066008  0.022552       NaN       NaN       NaN       NaN   \n",
       "feat_90  0.019803  0.014696  0.031679  0.027764       NaN       NaN       NaN   \n",
       "feat_91  0.024005  0.028850  0.033653  0.015917  0.014812       NaN       NaN   \n",
       "feat_92  0.049393  0.001424  0.070120  0.129622  0.035311  0.104226       NaN   \n",
       "feat_93  0.029035  0.499990  0.008631  0.030650  0.039864  0.000045  0.003653   \n",
       "\n",
       "         feat_93  \n",
       "feat_1       NaN  \n",
       "feat_2       NaN  \n",
       "feat_3       NaN  \n",
       "feat_4       NaN  \n",
       "feat_5       NaN  \n",
       "feat_6       NaN  \n",
       "feat_7       NaN  \n",
       "feat_8       NaN  \n",
       "feat_9       NaN  \n",
       "feat_10      NaN  \n",
       "feat_11      NaN  \n",
       "feat_12      NaN  \n",
       "feat_13      NaN  \n",
       "feat_14      NaN  \n",
       "feat_15      NaN  \n",
       "feat_16      NaN  \n",
       "feat_17      NaN  \n",
       "feat_18      NaN  \n",
       "feat_19      NaN  \n",
       "feat_20      NaN  \n",
       "feat_21      NaN  \n",
       "feat_22      NaN  \n",
       "feat_23      NaN  \n",
       "feat_24      NaN  \n",
       "feat_25      NaN  \n",
       "feat_26      NaN  \n",
       "feat_27      NaN  \n",
       "feat_28      NaN  \n",
       "feat_29      NaN  \n",
       "feat_30      NaN  \n",
       "...          ...  \n",
       "feat_64      NaN  \n",
       "feat_65      NaN  \n",
       "feat_66      NaN  \n",
       "feat_67      NaN  \n",
       "feat_68      NaN  \n",
       "feat_69      NaN  \n",
       "feat_70      NaN  \n",
       "feat_71      NaN  \n",
       "feat_72      NaN  \n",
       "feat_73      NaN  \n",
       "feat_74      NaN  \n",
       "feat_75      NaN  \n",
       "feat_76      NaN  \n",
       "feat_77      NaN  \n",
       "feat_78      NaN  \n",
       "feat_79      NaN  \n",
       "feat_80      NaN  \n",
       "feat_81      NaN  \n",
       "feat_82      NaN  \n",
       "feat_83      NaN  \n",
       "feat_84      NaN  \n",
       "feat_85      NaN  \n",
       "feat_86      NaN  \n",
       "feat_87      NaN  \n",
       "feat_88      NaN  \n",
       "feat_89      NaN  \n",
       "feat_90      NaN  \n",
       "feat_91      NaN  \n",
       "feat_92      NaN  \n",
       "feat_93      NaN  \n",
       "\n",
       "[93 rows x 93 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape = (df_train.shape[1], df_train.shape[1])\n",
    "upper_matrix = np.tril(np.ones(shape)).astype(np.bool)\n",
    "np.fill_diagonal(upper_matrix, False)\n",
    "correlation = df_train.corr('pearson').abs().where(upper_matrix)\n",
    "correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtrando colunas\n",
    "\n",
    "A partir da matriz de correlação assima, buscamos agora identificar quais das colunas possuem uma forte correlação de acordo com a tabela a seguir.\n",
    "\n",
    "Como sugerido por [Makuka,2012](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3576830/)\n",
    "\n",
    " <center>Interpretação do resultado de correlação </center>\n",
    "\n",
    "|Valor absoluto|Significado|\n",
    "|---|---|\n",
    "|0.9 < v | Muito forte |\n",
    "|0.7 < v <= 0.9 | Forte |\n",
    "|0.5 < v <= 0.7 | Moderada |\n",
    "|0.3 < v <= 0.5 | Fraca |\n",
    "|0.0 < v <= 0.3 | Desprezível |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WMesC5Q9pinlcQE8AJOspVakgp6QrpXNR6HEU+7hLaZiSViIQSVtK54vUaHWZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe673102f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "strong_correlation = correlation.where(correlation > 0.8)\n",
    "strong_correlation = strong_correlation.dropna(how='all', axis=(0,1))\n",
    "corr_features = strong_correlation[strong_correlation.notnull()].stack().index\n",
    "corr_features_size = len(corr_features)\n",
    "if corr_features_size:\n",
    "    col = math.floor(math.log2(corr_features_size)) or 1\n",
    "    row = math.ceil(corr_features_size/col)\n",
    "    figure, axis = plt.subplots(row, col, figsize=[15,2*row])\n",
    "    figure.tight_layout()\n",
    "    for idx, (feature1, feature2) in enumerate(corr_features):\n",
    "        if row == 1: # Has a single element\n",
    "            plot = axis.scatter(df_train[feature1],df_train[feature2])\n",
    "            plot = axis.set_xlabel(feature1)\n",
    "            plot = axis.set_ylabel(feature2)\n",
    "            plot = axis.annotate(strong_correlation[feature2][feature1],xy=(0,0))\n",
    "        elif col == 1: # Has multiples elements, but is a array\n",
    "            plot = axis[idx].scatter(df_train[feature1], df_train[feature2])\n",
    "            plot = axis[idx].set_xlabel(feature1)\n",
    "            plot = axis[idx].set_ylabel(feature2)\n",
    "            plot = axis[idx].annotate(strong_correlation[feature2][feature1],xy=(0,0))\n",
    "        else: # Multitle elements and is a matrix\n",
    "            plot = axis[int(idx/col), idx%col].scatter(df_train[feature1], df_train[feature2])\n",
    "            plot = axis[int(idx/col), idx%col].set_xlabel(feature1)\n",
    "            plot = axis[int(idx/col), idx%col].set_ylabel(feature2)\n",
    "            plot = axis[int(idx/col), idx%col].annotate(strong_correlation[feature2][feature1],xy=(0,0))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resultado\n",
    "\n",
    "A correlação mostra que não há uma fortíssima correlação entre as features, entretanto, há 10 colunas que estão fortemente correlacionadas. Porem buscamos uma correlação fortíssima para não remover features com comportamentos diferentes.\n",
    "\n",
    "# Train/Test split\n",
    "\n",
    "Utilizaremos 80% da base de treino para efetivamente treinar o modelo e 20% para averiguar a performance do modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = df_train\n",
    "y = df_target.categories\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling\n",
    "\n",
    "Trata-se do processo de transformar todos os dados da amostra para uma unidade padrão, neste problema utilizaremos a técnica de padronização que consiste em remover a média dos dados e colocar todos na escala do desvio padrão [Wikipedia](https://en.wikipedia.org/wiki/Feature_scaling). Em que $\\bar{x}$ é a média e $\\sigma$ é o desvio padrão.\n",
    "\n",
    "\\begin{equation}\n",
    "    x' = \\frac{x - \\bar{x}}{\\sigma}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_X = StandardScaler()\n",
    "sc_X_train = sc_X.fit_transform(X_train)\n",
    "sc_X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature scaling foi aplicado nos dataframes de **features** e utilizado nos modelos, mas o resultado não apresentou mudança. Os modelos continuaram com exatamente as mesmas performances.\n",
    "\n",
    "## Confusion Matrix\n",
    "\n",
    "A matriz de confução é uma métrica para algorítmos supervisionados em que é possível estabelecer uma relação entre os acertos e erros durante a classificação do conjunto de amostras. Basicamente elabora-se uma matriz em que nas colunas e linhas são as possíveis classes. Cada célula traz a contagem de amostras que eram da Label X (coluna) e foram classificadas na Label Y (linha). Dessa forma, na matriz, a diagonal principal trará os acertos do classificador [Microsoft](https://docs.microsoft.com/pt-br/sql/analysis-services/data- mining/classification-matrix-analysis-services-data-mining).\n",
    "\n",
    " Veja o exemplo a seguir:\n",
    "\n",
    "|Classificador\\Real|Label 1|Label 2|Label 3|\n",
    "|---|-------|-------|-------|\n",
    "|**Label 1**|10|10|0|\n",
    "|**Label 2**|1|10|1|\n",
    "|**Label 3**|0|0|3|\n",
    "\n",
    "Plot para matriz de confusão encontrado em [Scikit](http://scikit- learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx- glr-auto-examples-model-selection-plot-confusion-matrix-py) e adaptado para o problema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    plt.figure(figsize=(11, 7))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelo Dummy Classifier\n",
    "\n",
    "Dummy Classifier é um modelo que faz predições usando regras simples.\n",
    "\n",
    "O dummy é importante para termos como parâmetro de comparação com outros modelos.Não pode ser utilizado em problemas reais porque ele é apenas para realizar comparações e trabalha com aleatoriedade e frequencia de repetições para realizar as predições.\n",
    "\n",
    "\n",
    "Usamos dois tipos de estratégia:\n",
    "\n",
    "* **Stratified**: realiza predições baseadas na distribuição das classes da base de treino. (Ex.: 10% A, 20% B, 50% C, 20% D)\n",
    "* **Most Frequent**: sempre prediz com a classe mais frequente na base de treino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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T2Qw85Czefe+D1Y7n9WmXrFEeLWnv+5EV88gX88gX81h9ew+vZcaM6bmrGrpu3i91P/jc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\nu+cgSVJH1x4dpP1SSruWszj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe64b20cac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "most_frequent train dataset score: 0.26\n",
      "Média: 0.26\n",
      "Desvio padrão: 0.0001\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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5ZMmazeC0z6pfJg+Hzt3kxt3nfwg/1qZaAdYeDCIqWhN87yE7T1zHp6BrMqV8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\nMzPLWU6EzMzMLGf9P3RSOdWBpZxpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe64b014ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stratified train dataset score: 0.17\n",
      "Média: 0.17\n",
      "Desvio padrão: 0.0051\n"
     ]
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "def dummies(X_train, y_train, X_test, y_test):\n",
    "    models = ['most_frequent', 'stratified']\n",
    "\n",
    "    for model in models:\n",
    "        clf = DummyClassifier(strategy=model)\n",
    "        clf.fit(X_train, y_train)\n",
    "        score = clf.score(X_train, y_train)\n",
    "        y_pred = clf.predict(X_test)\n",
    "        cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "        plot_confusion_matrix(cm, classes=model)\n",
    "        # Cross validation\n",
    "        accuracies = cross_val_score(estimator=clf, X=X_train, y=y_train, cv=10)\n",
    "        \n",
    "        add_results(model, clf.score(X_train, y_train), clf.score(X_test, y_test))\n",
    "        print(model, 'train dataset score: %.2f' % score)\n",
    "        print('Média: %.2f' % accuracies.mean())\n",
    "        print('Desvio padrão: %.4f' % accuracies.std())\n",
    "\n",
    "dummies(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boosting\n",
    "\n",
    "A definição de boosting é que até mesmo algorítmos fracos de machine larning podem se tornar potentes [(KEARNS, 1988)](https://www.cis.upenn.edu/~mkearns/papers/boostnote.pdf).\n",
    "\n",
    "Um algorítmo fraco de aprendizagem pode ser definido como modelos ou regras que não possuem boa acurácia ou aparentam ser ineficientes, tais como modelos *dummy*: mais frequente, estratificado, randômico. Já algorítmos de aprendizagem forte, são aqueles que apresentam uma boa taxa de acertos [(FREUND e SCHAPIRE)](http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=4BF3325D8222B3234BB95971FCAD8759?doi=10.1.1.56.9855&rep=rep1&type=pdf).\n",
    "\n",
    "**Exemplo - Corrida de cavalos**[(FREUND e SCHAPIRE)](http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=4BF3325D8222B3234BB95971FCAD8759?doi=10.1.1.56.9855&rep=rep1&type=pdf):\n",
    "\n",
    "    Como determinar em qual cavalor apostar, considerando um conjunto de dados disponíveis tais como informações do cavalo, do dono, das corridas anteriores e etc. Ao perguntar para especialistas cada um deles irá falar coisas distintas e ainda assim muito imprecisas (modelos fracos)! Mas seria possível utilizar as regras de aposta de cada especialista e gerar uma única regra que seja capaz de predizer o cavalor vencedor da corrida utilizando boost "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Descent\n",
    "\n",
    "![](http://matthewemery.ca/images/gradient_descent.gif)\n",
    "\n",
    "\n",
    "Um algorítmo de gradient descendent é uma forma de minimizar o valor de uma função interativamente, na qual são dados um conjunto de parametros e ela busca a partir daí o menor valor[(TOUSSAINT, 2012)](https://ipvs.informatik.uni- stuttgart.de/mlr/marc/notes/gradientDescent.pdf). De forma que:\n",
    "\n",
    "\\begin{equation}\n",
    "    y_{min} = F(x_1) > F(x_2) > F(x_3) > ... > F(x_n),\\ onde:\\ F(x_n) < precisão\n",
    "\\end{equation}\n",
    "\n",
    "Um pseudo algorítmo que pode ser proposto para um problema de gradient é:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    x = inital_value\n",
    "    step = 0.01\n",
    "    repita\n",
    "        xprev=x\n",
    "        x = xperv - step * F(xprev)\n",
    "    enquanto abs(x - xprev) > precisao"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost\n",
    "\n",
    "XGBoost é um algoritmo que implementa *gradient boosting* de Decision Trees de forma rápida e com alta performance.  **Gradient Boosting** é uma técnica de *machine learning* para problemas de regressão e classificação que produz um modelo de predição na forma de *ensemble* de modelos de predições fracas, normalmente árvores de decisões.  Boosting é um processo sequencial, mas como o `XGBoost` consegue implementá-lo de forma paralela?  Sabemos que cada árvore pode ser produzida apenas depois que produzida a árvore anterior, mas o processo de criar as árvores pode ser paralelizado utilizando todos os núcleos a disposição.\n",
    "\n",
    "## Model\n",
    "\n",
    "### Objective Function:\n",
    "\n",
    "\\begin{equation}\n",
    "    \\text{obj}(\\theta) = L(\\theta) + \\Omega(\\theta)\n",
    "\\end{equation}\n",
    "\n",
    "**L- Training Loss function**: Mede predição do modelo na base de treino.  (Métrica: *Mean Squared Error*(MSE))   \n",
    "**Omega- Regularization function **: Controla a complexidade do modelo (Ajuda a evitar o *Overfitting*)\n",
    "\n",
    "nota: As *objective functions* devem sempre possuir *training loss* e *regularization*\n",
    "\n",
    "![](https://raw.githubusercontent.com/dmlc/web- data/master/xgboost/model/step_fit.png)\n",
    "\n",
    "### CART\n",
    "\n",
    "Uso de *CARTs* (Classification And Regression Trees) no ensemble das árvores \n",
    "\n",
    "![](https://raw.githubusercontent.com/dmlc/web- data/master/xgboost/model/twocart.png)\n",
    "\n",
    "\n",
    "Modelo de ensemble de árvores IGUAL ao modelo Random Forest, mas onde está então a diferença?\n",
    "\n",
    "## Training\n",
    "\n",
    "### Additive\n",
    "\n",
    "Training:\n",
    "\n",
    "Precisamos agora melhorar os paramêtros da função de **Regularization**, mas como fazer isso? Fazer isso aqui é muito mais difícil do que em problemas de otimização tradicionais, onde você pode usar o gradiente para isso. Não é fácil treinar todas as árvores ao mesmo tempo. Em vez disso, usamos uma **estratégia aditiva**: consertamos o que aprendemos e adicionamos uma nova árvore de cada vez.\n",
    "\n",
    "\n",
    "\\begin{split}\\hat{y}_i^{(0)} &= 0\\\\\n",
    "    \\hat{y}_i^{(1)} &= f_1(x_i) = \\hat{y}_i^{(0)} + f_1(x_i)\\\\\n",
    "    \\hat{y}_i^{(2)} &= f_1(x_i) + f_2(x_i)= \\hat{y}_i^{(1)} + f_2(x_i)\\\\\n",
    "    &\\dots\\\\\n",
    "    \\hat{y}_i^{(t)} &= \\sum_{k=1}^t f_k(x_i)= \\hat{y}_i^{(t-1)} + f_t(x_i)\n",
    "\\end{split}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost fit\n",
      "XGBoost train score\n",
      "XGBoost test score\n",
      "XGBoost confusion matrix\n",
      "XGBoost cross validation\n",
      "XGBoost results\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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va8Q7g990OmqGPPHowzRr1oJ1v/xKbNw6Kuew4wJQKSyMFXFrWRG3lqWxceTL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\nkUyUIImIiIhk8v+qj2AYiOe5tgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe64ae97c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultado na base de treino 0.75\n",
      "Resultado Médio na base de teste: 0.74\n",
      "Desvio padrão: 0.0056\n",
      "CPU times: user 45min 30s, sys: 9.53 s, total: 45min 39s\n",
      "Wall time: 12min 23s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "def xgboost(X_train, y_train, X_test, y_test):    \n",
    "    xgbclf = XGBClassifier(\n",
    "        learning_rate=0.01,\n",
    "        n_estimators=140,\n",
    "        max_depth=6,\n",
    "        min_child_weight=6,\n",
    "        gamma=0,\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.8,\n",
    "        nthread=8,\n",
    "        scale_pos_weight=1\n",
    "        )\n",
    "    print('XGBoost fit')\n",
    "    xgbclf.fit(X_train, y_train)\n",
    "    print('XGBoost train score')\n",
    "    train_score = xgbclf.score(X_train, y_train)\n",
    "    print('XGBoost test score')\n",
    "    y_pred = xgbclf.predict(X_test)\n",
    "\n",
    "    print('XGBoost confusion matrix')\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "    print('XGBoost cross validation')\n",
    "    accuracies = cross_val_score(estimator=xgbclf, X=X_train, y=y_train, cv=10)\n",
    "    \n",
    "    print('XGBoost results')\n",
    "    add_results('xgboost', xgbclf.score(X_train, y_train), xgbclf.score(X_test, y_test))\n",
    "    \n",
    "    plot_confusion_matrix(cm, classes=xgbclf)\n",
    "    print('Resultado na base de treino %.2f' % train_score)\n",
    "    print('Resultado Médio na base de teste: %.2f' % accuracies.mean())\n",
    "    print('Desvio padrão: %.4f' % accuracies.std())\n",
    "    \n",
    "\n",
    "xgboost(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GridSearchCV\n",
    "\n",
    "A ferramenta GridSearch disponibilizada pelo Scikit, gera de forma exaustiva candidatos a partir de um grid de  parâmetros especificados com o atributo param_grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_params = [{\n",
    "    'max_depth': [40, 50, 60, 80, 100, 120],\n",
    "    'max_features': [70, 80, 90, 92],\n",
    "    'min_samples_leaf': [2, 5, 10, 20, 30, 40]\n",
    "}]\n",
    "\n",
    "xgb_params = [{\n",
    "    'max_depth': [4, 5, 6],\n",
    "    'min_child_weight': [4, 5, 6]\n",
    "}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20 ms, sys: 10 ms, total: 30 ms\n",
      "Wall time: 371 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "def search_params(classifier, params):\n",
    "    clf = classifier()\n",
    "    grid_search = GridSearchCV(estimator=clf,\n",
    "                              param_grid=params,\n",
    "                              cv = 10,\n",
    "                              n_jobs=-1)\n",
    "\n",
    "    grid_search = grid_search.fit(X_train, y_train)\n",
    "    print(grid_search.best_score_, grid_search.best_params_)\n",
    "    return grid_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aplicando GridSearchCV ao XGBClassifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.797705143227 {'max_depth': 6, 'min_child_weight': 6}\n",
      "CPU times: user 2min 44s, sys: 667 ms, total: 2min 45s\n",
      "Wall time: 1h 30min 54s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# Takes long time to run\n",
    "search_params(XGBClassifier, xgb_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aplicando GridSearchCV ao Decision Tree Classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.724374772736 {'max_depth': 50, 'max_features': 70, 'min_samples_leaf': 10}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.72437477273645512"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_params(DecisionTreeClassifier, dt_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trees\n",
    "\n",
    "## Decision Tree\n",
    "\n",
    "Os dados são separados recursivamente formando uma árvore de decisão baseada nas features.Pode-se definir uma árvore de decisão, conforme diz (MITCHELL, 1997), como um método para aproximar valores discretos em funções, onde a função de aprendizagem é representada por uma árvore de decisão. Tais árvores aprendidas podem ser representadas - a nível de código fonte - como conjuntos de estruturas condicionais \"se-então\" para melhorar a leitura e entendimento humano, de acordo com (MITCHELL, 1997).\n",
    "\n",
    "Estes algoritmos são muito utilizados, segundo (MITCHELL, 1997), na área de algoritmos de inferência indutiva, e dentre as aplicações de tais algoritmos, tem-se máquinas que aprenderam a diagnosticar casos da medicina, ou ainda, para avaliar o risco de inadimplência dos requerentes de créditos em bancos.\n",
    "\n",
    "Para visualizar de forma mais fácil a representação de uma árvore, a figura 3, representada abaixo, caracteriza uma árvore de decisão em que a máquina deve decidir com base nas variáveis do tempo (ensolarado, nublado ou chuvoso), se pode ou não ocorrer uma partida de tênis. Além das variáveis de tempo, tem-se outras variáveis que podem ser levadas em conta dependendo da condição climática local, como umidade (alta ou normal) e o vento (forte ou fraco).\n",
    "\n",
    "![Workflow Random forest](arvore_jogo_tenis.png)\n",
    "\n",
    "O algoritmo de árvores de decisão classifica instâncias ou dados, ordenando-os apartir da raiz da árvore, para os nós de suas folhas. Cada nó da árvore exemplifica uma pergunta (teste) de alguns - atributos - de instância, e cada ramo descendente de um nó corresponde para um dos possíveis valores de tal atributo (MITCHELL, 1997). Vale a pena citar: O algoritmo ID3 (QUINLAN, 1986) aprende sobre árvores de decisão construindo-as de cima para baixo (nó raiz para as ramificações) tentando buscar respostas para a pergunta \"Qual atributo devemos testar na raiz da árvore?\", sendo assim, cada atributo instanciado é calculado por meio de testes estatísticos, para determinar o quão bem (ótimo) tal atributo, isolado dos demais, classifica os exemplos de treinamento.\n",
    "\n",
    "Quando o melhor atributo é selecionado e utilizado como teste no nó principal da árvore, cria-se um descendente para cada valor admissível deste atributo e os exemplos de treinamento são sorteados para o nó filho mais apropriado. O processo inteiro é então repetido utilizando treinamentos associados a cada descendente para selecionar o melhor atributo para testar na árvore. Quando realizado dessa forma, o algoritmo tenta de forma “gulosa“3.4. O modelo 49 Figura 3 – Exemplo de árvore de decisão, sobre condições para realização de um jogo de tênis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'inner: 0.81 cross: 0.71 +/- 0.01'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def fit_tree(X_train, y_train, X_test, y_test, tree_description='decision_tree'):\n",
    "    tree_clf = DecisionTreeClassifier(max_features=70, min_samples_leaf=10, max_depth=40)\n",
    "    tree_clf.fit(X_train, y_train)\n",
    "\n",
    "    inner_score = tree_clf.score(X_train, y_train)\n",
    "    tree_fit = cross_val_score(tree_clf, X_train, y_train)\n",
    "    \n",
    "    add_results(tree_description, tree_clf.score(X_train, y_train), tree_clf.score(X_test, y_test))\n",
    "    \n",
    "    return inner_score, tree_fit.mean(), tree_fit.std()\n",
    "\n",
    "\"inner: {:.2f} cross: {:.2f} +/- {:.2f}\".format(*fit_tree(X_train, y_train, X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribuição dos dados\n",
    "\n",
    "Um dos modelos a ser utilizado será o decision tree no método de montagem random forest. Este modelo de predição possui um problema de viés quando uma das classes na base de treino é mais predominante do que outra, ou seja, a distribuição das classes na base de treino devem ser semelhantes para evitar problemas de [overfiting](http://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html).\n",
    "\n",
    "Para tanto, precisa-se descobrir qual a contagem de cada classe disponível na base de treino, montaremos um histograma para verificar a diferença entre elas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>categories</th>\n",
       "      <th>increase_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Class_1</th>\n",
       "      <td>1929</td>\n",
       "      <td>1929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_2</th>\n",
       "      <td>16122</td>\n",
       "      <td>16122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_3</th>\n",
       "      <td>8004</td>\n",
       "      <td>8004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_4</th>\n",
       "      <td>2691</td>\n",
       "      <td>2691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_5</th>\n",
       "      <td>2739</td>\n",
       "      <td>2739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_6</th>\n",
       "      <td>14135</td>\n",
       "      <td>14135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_7</th>\n",
       "      <td>2839</td>\n",
       "      <td>2839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_8</th>\n",
       "      <td>8464</td>\n",
       "      <td>8464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_9</th>\n",
       "      <td>4955</td>\n",
       "      <td>4955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         categories  increase_count\n",
       "target                             \n",
       "Class_1        1929            1929\n",
       "Class_2       16122           16122\n",
       "Class_3        8004            8004\n",
       "Class_4        2691            2691\n",
       "Class_5        2739            2739\n",
       "Class_6       14135           14135\n",
       "Class_7        2839            2839\n",
       "Class_8        8464            8464\n",
       "Class_9        4955            4955"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = [0] *len(df_target.target.cat.categories)\n",
    "\n",
    "def reduce(target):\n",
    "    counts[target.categories] += 1\n",
    "    return counts[target.categories]\n",
    "\n",
    "df_target['increase_count'] = df_target.apply(reduce, axis=1)\n",
    "df_target.groupby('target').count()\n",
    "df_target.groupby('target')['increase_count'].max().sum() == df_target.target.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtrar dados\n",
    "\n",
    "Agora, iremos filtrar os dados deixando apenas os primeiros registros. O critério de filtrar os dados será pegar a classe que possue o menor número e utilizar ele como base para remover os demais, considerando um tamanho máximo de até 2x o da menor classe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>categories</th>\n",
       "      <th>increase_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Class_1</th>\n",
       "      <td>1929</td>\n",
       "      <td>1929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_2</th>\n",
       "      <td>3857</td>\n",
       "      <td>3857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_3</th>\n",
       "      <td>3857</td>\n",
       "      <td>3857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_4</th>\n",
       "      <td>2691</td>\n",
       "      <td>2691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_5</th>\n",
       "      <td>2739</td>\n",
       "      <td>2739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_6</th>\n",
       "      <td>3857</td>\n",
       "      <td>3857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_7</th>\n",
       "      <td>2839</td>\n",
       "      <td>2839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_8</th>\n",
       "      <td>3857</td>\n",
       "      <td>3857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class_9</th>\n",
       "      <td>3857</td>\n",
       "      <td>3857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         categories  increase_count\n",
       "target                             \n",
       "Class_1        1929            1929\n",
       "Class_2        3857            3857\n",
       "Class_3        3857            3857\n",
       "Class_4        2691            2691\n",
       "Class_5        2739            2739\n",
       "Class_6        3857            3857\n",
       "Class_7        2839            2839\n",
       "Class_8        3857            3857\n",
       "Class_9        3857            3857"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_percent = 2\n",
    "minimum_value = df_target.groupby('target')['increase_count'].max().min()\n",
    "df_rtarget = df_target[ df_target.increase_count < minimum_value*distance_percent ]\n",
    "df_rtarget.groupby('target').count()\n",
    "df_rtrain = df_train.drop( df_target[df_target.increase_count >= minimum_value * distance_percent].index )\n",
    "df_rtrain.shape[0] == df_rtarget.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verificando resultado\n",
    "\n",
    "Após aplicar uma melhor distribuição nos dados, rodou-se novamene o algorítmo da decision tree e percebeu-se que a acurácia do modelo diminuiu, e portanto, não será utilizado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'inner: 0.77 cross: 0.65 +/- 0.00'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tr, X_te, y_tr, y_te = train_test_split(df_rtrain, df_rtarget.target, test_size=0.2)\n",
    "\"inner: {:.2f} cross: {:.2f} +/- {:.2f}\".format(*fit_tree(X_tr, y_tr, X_te, y_te))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest\n",
    "\n",
    "Breiman breiman, 2001, descreve Random Forests como uma evolução das decisions trees, onde várias ávores são formadas para criar um modelo com maior precisão.  Isto é feito a partir da separação dos Dados em conjutos de dados menores e aleatórios. Cada árvore é contruida a partir de um pedaço aleatório dos dados. Quando um novo dado chega, a predição é feita por todas as Árvores e ao fim é feita uma votação por maioria, ou seja, a categoria com mais votos ganha e o resultado é dado.\n",
    "\n",
    "![Workflow Randomforest](forest.jpg)\n",
    "\n",
    "De acordo com breiman, 2001, as RFs corrigem a maior parte dos problemas de Overfitting que as Árvores de decisão apresentam. Tudo depende do quanto as DT contidas dentro da Random Forest. Isto é, o quanto elas representam os dados.\n",
    "\n",
    "## Utilizando o algoritmo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('random_forest', 0.9920003232192639, 0.77957336780866193)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "('random_forest_otimized', 0.84002666558926908, 0.7690691661279897)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "def test_random(params, X_train, y_train, X_test, y_test, name='random_forest'):\n",
    "    rfclf = RandomForestClassifier(**params)\n",
    "    rfclf = rfclf.fit(X_train, y_train)\n",
    "    \n",
    "    train_score = rfclf.score(X_train, y_train)\n",
    "    test_score = rfclf.score(X_test, y_test)\n",
    "\n",
    "    add_results(name, train_score, test_score)\n",
    "    return name, train_score, test_score\n",
    "params = {'n_estimators': 10, 'max_features': 70, 'min_samples_leaf': 10, 'max_depth': 40}\n",
    "test_random({}, X_train, y_train, X_test, y_test)\n",
    "test_random(params, X_train, y_train, X_test, y_test, 'random_forest_otimized')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verificando com Cross Validation\n",
    "\n",
    "Cross validation irá predizer um pedaço do dataset utilizando o modelo treinado com o resto dos dados que não fazem parte deste dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=40, max_features=70, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=10, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76.09793654862037 de precisão\n"
     ]
    }
   ],
   "source": [
    "rfclf = RandomForestClassifier(**params)\n",
    "rfclf.fit(X_train, y_train)\n",
    "rfscores = cross_val_score(rfclf, X_train, y_train)\n",
    "print (\"{} de precisão\".format(rfscores.mean() * 100))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importancia das features para a RF\n",
    "\n",
    "A seguir vemos quais as influências de cada uma das features para o uso no random forest. Quanto maior no gráfico, maior é a importância da feature.\n",
    "\n",
    "### Gini\n",
    "\n",
    "O método utilizado para gerar a importância das features no modelo é a Decrease Mean Importance, que utiliza em seus cálculos um indicador de impureza no sistema. No caso do random forest implementado [(LOUPPE et al.,2013)](https://pdfs.semanticscholar.org/2635/19c5a43fbf981da5ba873062219c50fdf56d.pdf), este indicador é o Gini Impurity que pode ser entendido como uma redução da probabilidade de errar a classificação de uma categoria dentro de um algorítmo de árvore [(Sebastian Raschaka)](https://sebastianraschka.com/faq/docs/decision-tree-binary.html).\n",
    "\n",
    "#### O indice\n",
    "\n",
    "O indice de Gini pode ser calculado utilizando a seguinte fórmula[(TEKIMONO,2009)](http://people.revoledu.com/kardi/tutorial/DecisionTree/how-\n",
    "to-measure-impurity.htm):\n",
    "\n",
    "\\begin{equation}\n",
    "    Gini = 1- \\sum_{i=1} p_i^2\n",
    "\\end{equation}\n",
    "\n",
    "Em que $p_i$ é a probabilidade da ocorrência de uma determinada classe, desconsiderando os atributos. Ou seja $N_i$ é o número de ocorrências da classe i e N é o total de elementos das classes:\n",
    "\n",
    "\\begin{equation}\n",
    "    p_i = \\frac{N_i}{N}\n",
    "\\end{equation}\n",
    "\n",
    "#### Para Decisions Trees\n",
    "\n",
    "Para Classification and Regression Trees (CART), utiliza-se o indice de Gini modificado, isto é, calcula-se ainda as probabilidades em $p_i$, mas agora utiliza-se do indice de Gini nos filhos da esquerda $t_l$ e direita $t_r$. Recalcula-se as probabilidades para ambos os nós também em $p_l$ e $p_r$ utilizando como base as possíveis classes reduzidas a $N_t$ [(LOUPPE et al.,2013)](https://pdfs.semanticscholar.org/2635/19c5a43fbf981da5ba873062219c50fdf56d.pdf).\n",
    "\n",
    "\\begin{equation}\n",
    "    i(s, t) = Gini(t) - p_l Gini(t_l) - p_r Gini(t_r) \\\\\n",
    "    p(t) = \\frac{N_{l|r}}{N_t}\n",
    "\\end{equation}\n",
    "\n",
    "#### Decrease Mean Importance\n",
    "\n",
    "Para calcular a importância de uma feature X ao tentar predizer uma label Y, utiliza- se os indices de impureza com a proporção de $N_f$ amostras em relação ao total $N$.  $N_T$ é o total de árvores na floresta. Assim, para uma Random Forest a conta é:\n",
    "\n",
    "\\begin{equation}\n",
    "    I(X_m) = \\frac{1}{N_T} \\sum_{T} \\sum_{t \\epsilon T:v(s)=X_m} pf(t)i(s,t) \\\\\n",
    "    pf(f) = \\frac{N_f}{N}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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67psPAS+kfCjzQspZAS+oU+d+XMMjr68d69jXTfce0L0fKV8NOJxyBH8T8O/A\nScDjeeR1vZDyn68FHft16uedmrcD8B/18T51bPfjgPajr2vfH92P828/4vvjvHh/nG77kzb5HcHm\nvJrygr+sa1oLPFiXWZSZ6wAy80LKpxsfjIiXA+Vfc/1+IOUQNXXegrruLZl5LPBF4JKIOGaWPDtn\n5uV1WzdSXpx3dXyX7zbKkUko/8CntpuZ+dXMfHNm7g/8GuXTkf+KiC8Cl0fEhymf4nym5v+liDiH\n8qnYgnzkE6BbKG/UZOYlwJ7A/RHxzGnyPhO4n/LpzwGZuXPX9HjKmxaUF/HmOu7/UD7V2TkiPkZ5\nY52qPQSsycwH6/PNwMOZeRTlCOvZlE+yNgAPZebNmXkzsH1E/GREHFp/nns7xtsC/EdEnB4R29fH\nP1//nl4E3Dm1H+s6X8/Mt2XmQZTTDR5HeePZJiKeAOwN7BQRS+oYu9ef4eGO74Y+mfIGT2beQXnT\ncz8OZj8+XOd3e1Kt/TDl08aXTTPdXpfdJjPvqeNuoPyCPSIi3lm3sTkzt2TmfcA3MvOuuuz/1m0s\npbxXvBm4MzP/A/jfzPxcZn5u6mesP1NMvb7q/twMfDUiXl2zXBURS+s+eCrlSHxdPB/OzE9l5mvq\nz/w+YDnlDIBtImIR5Zf1DsAudb3tKL8QobxPTM3bqQ56S627HwezH31d+/7ofpx/+9H3x3ny/jjN\ntidPjkE3Oh8nyuH1n5qhdlP9cy3l8HtnbS/KJ0p3U/4xP26a9ZcAv9w1b0fK+emf75h3H498qnI3\nj5y7vg2lSf0JyhG+8+r0DeDvaq5X1mWvmOFnCMqnRgsppzgcXx//FOUc7z+omc6lfBL0S5RTLN5Z\n19+B8gnbIZRTSK+lHCH8FOVUhy9RTiX9E2DZDBnOqH/+G/CCaep/QnmhfpKuc+hr/YnAV7r24Tsp\np1Rs7Jj/2a7pSXX+7nVfbQucRml0b6nbvJtyOso+s+3Hjm2cQPlE7DvAK4BPU07nuJXy3YbjKKeq\nXFK3cWRdb3HdjvtxMPtxOeXI9ycp/9E4m/IJ5vpaOwd43gzb/nDHa//grtpCymtsS/17mjqC33kE\nfRcefRR/L8oR/zOBWzrmb6D8Er2p/jm1H3eivHfsAvw95fX8Zcov5RuBz1H+4zTrfqS8Nn+nrnMz\n5XsenwH+lvJe8hbgDZT3lr+lvI5f3bEfP+9+HNh+9HXt+2PT+/HTW7EfD52A/fgfI9qP/p5p8fvj\nJE9+R7Ah9dOg+7N8GjPTMi8BNmXmVV3zdwFOycw/7XNbH8/MV0wz/yn14fspX7D9VmY+FBF7AM/P\nzH+qRxcPp5zusJBynvXFWT5oGD+pAAANtklEQVQZISJemZkf7ifHDNm2pRxFfCPlFNZzM3NL/TTu\nh7J8gkdEPJFyhBDKqRb/byu2sT18/5Ou7tqemXnrDOvtSDlt5Ltd858BPCcz399juwuA7Tr/juvf\n3cLMvL1r2Z2yfnrXY7zIcs79Qsp5/7dm5rdrfTfKab3rp/5+phnD/TjH/ViPwi+jYz9SPgncMtt2\nO9bfi/Jp7A/s+4h4LuX7tw9MU9uD8sv2mq75R1IuXPBHPba7A+V7wTfV5ztTTsdeSPmP0nc6ln1q\nZn69x3hPBsjMb0W5KvBLKP9R+EqtH0T5fsxXM/Nr06zvfmTu+7Eu4+u6ve+P21A+EHY/zqN/j74/\nfn+ZefX+OJGG0W06zTwBH5/rMvT+9KpXvdf4w8j4A+vTdXXRra0PYoym64PeBjNcgbbf+iDGaLre\n9DYoR8y3qY8XUT513K1juVnrgxij6fqwttG1vdf2+Hc8a30QYzRdb3obdF35eWvrgxij6XoT22Dm\nK2Qv71HvvIL2rMuMuj6kDL2uIj5rfRBjNF0f4jb26fj3uYRynYiDetSf3scYTx+X+hC3MeNV6fup\nD2qMSZ1GHqDtEz0apH6WoeuLuo+hPqdGckAZf2B9Ok5TmGGdWeuDGKPp+qC2QfmFvZHyXc9P0XEZ\nZMqXwmet1z/nNEbT9SFl7HXLllnrdZw5jdF0fUgZ3zjNdNsM89/INLe+6bXMY6hvVYZe6zeUsbv+\nvo5/n8+jvNY/S7k4xs/2qtf5cxqj6fqQMl7FI1+N+H3K9+r/mHLq3p/3qK+s8+cyxlzrK3utP4SM\nKymnHN4AvA04cJrfRbPW+1lm1PUhZZj1tlu96oMYo+n6kDK+gHK676eBOyineX6Bcnrw3r3qNeOc\nx5j0aepLmBqdHNAyTWZoLGNEvLc+3LPjMZTvIO7aNY/uetcY0y4z6vowMgJvp1wWe12UiwZdEhEn\nZuaX6jK96gxgjKbrw8j4Fsrlv2e6ZcuCHvVPDGCMpuvDyDjdrW0WUC4KAOW+ULPVodyXai5jNF0f\nRsZnd2zrbcDLM/PyiNiPcrn6bXrUVw9gjKbrw8i4IMuFOqB8f+unM/N/I2Il5QOgbXrUVwxgjKbr\nbxpCxqsptw04AVgVEfdSbgtwQZYLlvSqM4Axmq4PI+OJlPv6zXTbrehRf+cAxmi6PoyMJ1Lua7gp\nIvalXIPiuRFxGKVhXNyjfjjldmtzHWOyjboTbftEjyNl/SzD3E8NndMRxblkpHxJ+2TKi/ykrum2\njnp37STgtq4xpl1m1PUhZbyqa78eRLmv0sspv8Bnrdd5cxqj6fqQMva6Zcus9e5/649ljKbrQ8rY\n69Y2s9b7WWbU9SFl6LyoQ/el7i/vVR/EGE3Xh5Txi9TTySgX5Zg66vU4yoXTZq3Xx3Mao+n6kDJ2\n3yZgGeU//BvrurPWu/+uH8sYTdeHlLHXbbdmrdc/5zRG0/VhZuxYpvN9YF2vemfGuYwx6dPIA7R9\nor/TLjdOM+8NHY/f16N++FwyDCjjtBmoV1edbhuUUwL6ufrqrMuMuj6kjL2uQDtrvT6f0xhN14eU\n8Qoe+d7bso5lFvDIfTFnrE+9XuYyRtP1YWTseH405TSaY+hqovqpD2KMputNboPeV36etT6IMZqu\nDynjrFfI7lWvY81pjKbrQ8rY6yris9an3nvmMkbT9SFl/HvK1UP/lXKk8HzKldXPoRzBnrVex5rT\nGE3Xh5Sx11XpZ63Xx3MeY9KnkQeY7xMdzdB08yiHlXstc8M09UfdNLVHvdf4PRvJAWScdn1gt/qC\nmqlR3I36Sfks+3jWZUZdH1KGl1Av2dw1fxfKfYJmrQ9ijKbrQ8o46y1betXr4zmN0XR9GBm75v3A\nrW22pj6IMZquN7UNHrlJ9tS0bZ2/B/ALveqDGKPp+jAy1scLgCMov3d+l3Lq464d+3rW+iDGaLre\n9DaoDecsr4FZ64MYo+n6kDL0uu3WrPVBjNF0fUgZtwVeW+f9Go/cfH576vvAbPX6eM5jTPo08gDz\nfaJHkzbbMvUF8AnKF1RXdUyfpVyYYdZ6vxmazLg12+ixH4dxZdOR1schgxnNOE4ZzGjGccpgRjOO\nUwYzTk7GcZ68WExDIuIEyqkU+0bEqo7S44Hv9bnMFylX5dsDeEdH/W7KaTB7zlbvNf4wMvazjT7t\nN8f6IMZouj4OGcw4nPo4ZDDjcOrjkMGMw6mPQwYzDqc+DhnMOJz6oMYYSzaCzenVxPVcJjM3AzcD\nz5lhGzfPVo+IOTWSg8jYR4Z+5Rzrgxij6fo4ZDDjcOrjkMGMw6mPQwYzDqc+DhnMOJz6OGQw43Dq\ngxpjLNkINiQzZ23S+l0GICKeDfwV8GOUG74uAO7NzJ37qD/mRnKAGXuuL0mSJGl4tum9iOYiIp4d\nEWsi4p6IeDAitkTEXVu5zJmU7+LdQPmC6q8CZ/Vb7zX+MDL2s41eu3KO9UGM0XR9HDKYcTj1cchg\nxuHUxyGDGYdTH4cMZhxOfRwymHE49UGNMZZsBJvXq4nra5nMXE+5WtGWzPw7YPlW1OfUSA4oY69G\n8Q1d2+ue99896n84gDGarpvRjGY0oxnNaEYzmnF+ZfzD7vrEGPXVaub7BKytf3belLL7ipyzLgN8\nnnK65XnA24HfoePG2H3Ue40/jIy91m/syqbjUh+HDGY0oxnNaEYzmtGMZhxcxkme/I5g8+6LiEXA\nlRHxdsqFU7qPxPZa5sT6/BRKg7U38IqtqPcafxgZp10/hnBl01HXzWhGM5rRjGY0oxnNOL8yMg/Y\nCDavV4PUc5nMvDkitgeelJlv7d5Ar3ofGRrPOMv6D9HwlU37GKPpuhnNaEYzmtGMZjSjGedXxokX\n9fCmGlQbpH0y8/rHskxEvAz4S2BRZu4bEQcDp2fmUf3U+8nQdMZ+tyFJkiRpCEZ9bup8n4CXAdcD\nN9XnBwOrtmYZ4DJgFx59vvI1W1HvNf4wMvZa/9nAGuAe4EFgC3BXv/VBjNF0fRwymNGMZjSjGc1o\nRjOacXAZJ3kaeYD5PtGjQepnGeBL9c/O+tVbUZ9TIzmgjL3WXwvsD1xBuQfhq4E/77c+iDGaro9D\nBjOa0YxmNKMZzWhGMw4u4yRPIw8w3yd6NEj9LAOcQ/my6tXAAZQbt79/K+pzaiQHlLHX+sO4sulI\n6+OQwYxmNKMZzWhGM5rRjIPLOMmTF4tp3rqIeCWwICIOAF5P+XJqz2Ui4vzMPBH4BnAQ8ADwj8DF\nwNt61bciQ2MZt2Ibw7iy6ajr45DBjGY0oxnNaEYzmtGMgx1jIs2LH2IcRcT59WF3g3QX8Nt9LnNo\nRDwZOI5ytaKXAofXxzv0qvcafxgZ+9lG1XlV0XuZ/cqm09UHMUbT9XHIYEYzmtGMZjSjGc1oxsGO\nMZlGfUhyvk7AtcCTgauA3bqnfpahHDW7jtI83dgx3VT/7FXvNf4wMvbcRsc+2x542iz7dNb6IMZo\nuj4OGcxoxnHKYEYzjlMGM5pxnDKYcXIyTuo08gDzdaJHg9TvMnW5v+6xrWnrvcYfRsatWH8YVzYd\naX0cMpjRjGY0oxnNaEYzmnFwGSd5GnmA+T7Ro4nrd5kmMwwjYx8ZhnFl05HWxyGDGc1oRjOa0Yxm\nNKMZB5dxkie/I9iwzPzNQSzTZIZhZOxj/Ycy887u1baiPogxmq6PQwYzmtGMZjSjGc1oRjMOdoyJ\n5FVDNS4au7LpGNXHIYMZzWhGM5rRjGY0oxkHO8ZE8oigRiqGcGXTUdfNaEYzmtGMZjSjGc04vzIy\nH4z63FSndk8M58qmI62b0YxmNKMZzWhGM5pxfmUc9f+hBzF5aqhG7f3AZ4D9gLUd84Ny/vW7e9T3\nG8AYTdfNaEYzmtGMZjSjGc04vzLux6QbdSfq5JSZMIQrm466Pg4ZzGjGccpgRjOOUwYzmnGcMphx\ncjJO8hT1B5QkSZIktYQXi5EkSZKklrERlCRJkqSWsRGUJAmIiNdHxHUR8Q9bud6Seo8pSZImho2g\nJEnFa4HDMvOXtnK9JcBWN4IRsWBr15EkaVBsBCVJrRcR76dcCvyTEfHmiDg3Ir4SEVdExNF1mSUR\n8Z8RcXmdfqquvhL46Yi4MiJ+JyJeFRFndoz9bxHxwvr4noh4R0RcBTwnIg6NiM9FxGURcXFEPGm4\nP7kkqa1sBCVJrZeZvwF8C3gRsCNwaWYuq8//IiJ2BL5LOWJ4CHAc8N66+grgPzPz4Mx8V49N7Qh8\nOTOfAXwZ+CvgmMw8FDgX+NMB/2iSJE3LG8pLkvRohwNHRcTv1eePA/ahNIpnRsTBwBbgqY9h7C3A\nx+vjpwFPBy6JCIAFwLfnkFuSpL7ZCEqS9GgBvCIzr3/UzIjTgO8Az6CcUXP/DOtv5tFn3Dyu4/H9\nmbmlYzvrMvM5gwgtSdLW8NRQSZIe7WLgt6IepouIn6zzdwG+nZkPAydSjuAB3A08vmP9DcDBEbFN\nROwNLJthO9cDiyPiOXU720bEQQP9SSRJmoGNoCRJj/Y2YFvg6ohYV58DvA84qV7o5UeBe+v8q4Et\nEXFVRPwO8AXgJuBayvcIL59uI5n5IHAMcEYd80rgp6ZbVpKkQYvMHHUGSZIkSdIQeURQkiRJklrG\nRlCSJEmSWsZGUJIkSZJaxkZQkiRJklrGRlCSJEmSWsZGUJIkSZJaxkZQkiRJklrGRlCSJEmSWub/\nA6EOadJM/4ysAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe64b0e3d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis = plt.subplots(figsize=(15, 5))\n",
    "plot = axis.bar(df_train.columns, rfclf.feature_importances_)\n",
    "plot = axis.set_xticklabels(df_train.columns.values, rotation='vertical')\n",
    "plot = axis.set_xlabel('feature')\n",
    "plot = axis.set_ylabel('importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ExtraTrees\n",
    "\n",
    "O [Scikit Learn](http://scikit- learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html) nos apresenta um tipo diferente de random forest que pode apresentar resultados melhores que o [RandomForestClassifier](http://scikit- learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html).  Assim como afirma que as extra tree devem ser utilizadas apenas em algorítmos de montagem Como o Extra Trees Classifier e Regressor.\n",
    "\n",
    "O que diferencia uma extra tree de uma decision tree é a forma que é feita a construção da árvore. Enquanto uma decision tree utiliza cópia dos dados e sub amostras para realizar as divisões de cada nó. Uma extra tree utiliza um ponto de divisão randomico e utiliza toda a base de treino para crescer a árvore [(GEURTS, ERNST e WEHENKEL, 2005)](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.65.7485&rep=rep1&type=pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76.8898405821295 de precisão\n",
      "Inner score 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "etc = ExtraTreesClassifier()\n",
    "etscores = cross_val_score(etc, X_train, y_train)\n",
    "print (\"{} de precisão\".format((etscores.mean() * 100)))\n",
    "\n",
    "etc = etc.fit(X_train, y_train)\n",
    "add_results('extra_trees', etc.score(X_train, y_train), etc.score(X_test, y_test))\n",
    "print(\"Inner score\", etc.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neurônio Artificial\n",
    "\n",
    "![Workflow NeuralNetwork](neural.jpg)\n",
    "\n",
    "#### Entrada\n",
    "\n",
    "- Sinais de entrada {x1,x2,...,xn}.\n",
    "- Cada sinal de entrada e ponderado por 1 peso.{w1,w2,...,wn}.\n",
    "- O peso é adquirido a partir do treino.\n",
    "\n",
    "#### Função agregadora\n",
    "\n",
    "- Recebe todos os sinais e realiza a soma dos produtos dos sinais.\n",
    "\n",
    "#### Neurônio\n",
    "\n",
    "- Tem a função de deixar, passar ou inibir um sinal de saida de acordo com a entrada.\n",
    "- Teta é a limiar de ativacao(ponderado),'u' é o potencial de ativação que é passado para a função (g(u)), que é a função de ativação que é responsavel pela saida que permite o sinal passar ou não ou até mesmo modificalo.\n",
    "\n",
    "#### Formula\n",
    "\n",
    "- Potencial de ativação\n",
    "\n",
    "![Workflow Potencialdeativacao](formula.png)\n",
    "\n",
    "### MLP Classifier\n",
    "\n",
    "Esse algoritmo é um classificador Perceptron de Multicamadas usado para fazer o treinamento de modelos, e é uma biblioteca do Scikit-Learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score treino:  0.87135873298\n",
      "Score teste:  0.785956690368\n",
      "Score: 0.7825550109810641 +/- 0.007188504494548886\n",
      "CPU times: user 4min 13s, sys: 4min 39s, total: 8min 52s\n",
      "Wall time: 2min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "mlp = MLPClassifier(solver='adam',activation='relu',max_iter=250)\n",
    "mlp.fit(X_train, y_train)\n",
    "saidas = mlp.predict(X_test)\n",
    "scoreTreino =  mlp.score(X_train, y_train)\n",
    "scoreTeste =  mlp.score(X_test, y_test)\n",
    "\n",
    "print('Score treino: ', scoreTreino)\n",
    "print('Score teste: ', scoreTeste)\n",
    "\n",
    "mlpscores = cross_val_score(mlp, X_train, y_train)\n",
    "\n",
    "print('Score: {} +/- {}'.format(mlpscores.mean(), mlpscores.std()))\n",
    "\n",
    "add_results('multi_layer_perceptron', scoreTreino, scoreTeste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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eAUkVJBEREZHMVEESERGRrBnfnIOkDpKIiIi45YsdJA2xiYiIiGSiCpKIiIi4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe64b1e5da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "mp = confusion_matrix(y_test,saidas);\n",
    "plot_confusion_matrix(mp, classes=mlp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessamento de dados\n",
    "\n",
    "A rede neural pode ter dificuldade em convergir antes de atingir o número máximo\n",
    "de iterações permitido se os dados não forem normalizados. Multi-layer\n",
    "Perceptron é sensível ao dimensionamento de features, portanto, é altamente\n",
    "recomendável dimensionar seus dados. Usaremos o StandardScaler incorporado para\n",
    "padronização."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 107 ms, sys: 3.33 ms, total: 110 ms\n",
      "Wall time: 109 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "# Fit only to the training data\n",
    "scaler.fit(X_train)\n",
    "\n",
    "# Now apply the transformations to the data:\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 21s, sys: 2min 14s, total: 3min 35s\n",
      "Wall time: 57.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))\n",
    "mlp.fit(X_train,y_train)\n",
    "predictions = mlp.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.61      0.44      0.51       388\n",
      "          1       0.73      0.79      0.75      3247\n",
      "          2       0.53      0.50      0.52      1618\n",
      "          3       0.59      0.51      0.55       543\n",
      "          4       0.95      0.95      0.95       536\n",
      "          5       0.94      0.93      0.94      2787\n",
      "          6       0.70      0.60      0.65       579\n",
      "          7       0.88      0.92      0.90      1677\n",
      "          8       0.83      0.87      0.85      1001\n",
      "\n",
      "avg / total       0.78      0.78      0.78     12376\n",
      "\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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PIzw8/518ccZXX0xIG14D2LN7NwCnT5/m1ZeGMWBQpmejF0wOzUFyki/TeXXNAWPMrWeu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\n6734cwZYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe62bd0b748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "TypeError",
     "evalue": "add_results() takes 3 positional arguments but 5 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-e18e72c3be86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplot_confusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmlp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0madd_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'MultiLayerPerceptron parametrized'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: add_results() takes 3 positional arguments but 5 were given"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "print(classification_report(y_test,predictions))\n",
    "mp = confusion_matrix(y_test,predictions)\n",
    "plot_confusion_matrix(mp, classes=mlp)\n",
    "add_results('MultiLayerPerceptron parametrized', X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "coefs\\_ é uma lista de matrizes de peso, onde a matriz de peso no índice i\n",
    "representa os pesos entre a camada i e a camada i + 1.\n",
    "\n",
    "intercepts\\_ é uma lista de vetores de polarização, onde o vetor no índice i\n",
    "representa os valores de polarização adicionados à camada i + 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 93 30\n"
     ]
    }
   ],
   "source": [
    "print(len(mlp.coefs_), len(mlp.coefs_[0]), len(mlp.intercepts_[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusão\n",
    "\n",
    "Como conclusão, tivemos a utilização do modelo Random Forest e Extreme Gradient Boosting otimizados. Mas o gráfico a seguir irá mostrar os resultados com a base de treino e base de teste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiRVYMiSJEmqwJAlSZJUgSFLkiSpAkOWJElSBYYsSZKkCgxZkiRJFRiyJEmSKjBkSZIk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\nH1QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe62bceba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = [x.replace('_',' ') for x in results.keys()]\n",
    "train = []\n",
    "test = []\n",
    "width=0.4\n",
    "base = np.arange(len(columns))\n",
    "for key in results:\n",
    "    train.append(results[key]['train'])\n",
    "    test.append(results[key]['test'])\n",
    "fig, ax=plt.subplots(figsize=[10,10])\n",
    "fig = ax.bar(base, train, width)\n",
    "fig = ax.bar(base+width, test, width)\n",
    "fig = ax.set_xticks(base+width/2)\n",
    "fig = ax.set_xticklabels(columns, rotation='45')\n",
    "fig = ax.legend(['Base de treino', 'Base de teste'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Referências Bibliográficas\n",
    "\n",
    "http://scikit- learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier\n",
    "\n",
    "https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning- xgboost-with-codes-python/\n",
    "\n",
    "http://scikit- learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html\n",
    "\n",
    "ftp://ftp.sas.com/pub/neural/FAQ3.html#A_hu\n",
    "\n",
    "[MITCHELL](https://dl.acm.org/citation.cfm?id=505283), Tom M. Machine learning.  1997. Burr Ridge, IL: McGraw Hill, v. 45, n. 37, p. 870-877, 1997.\n",
    "[QUINLAN](http://hunch.net/~coms-4771/quinlan.pdf), J.. Ross . Induction of decision trees. Machine learning, v. 1, n. 1, p. 81-106, 1986.\n",
    "[BREIMAN](https://www.stat.berkeley.edu/users/breiman/randomforest2001.pdf), Leo. Random forests. Machine learning, v. 45, n. 1, p. 5-32, 2001.\n",
    "\n",
    "BABATUNDE, Oluleye, ARMSTRONG, Leisa, DIEPEVEEN, Dean e LENG, J. Comparative analysis of Genetic Algorithm and Particle Swam Optimization: An application in precision agriculture. 2015. **Asian Journal of Computer and Information Systems**. 3.  1-12."
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}