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Sub - Inteligencia Computacional
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| precipitacao | temperatura | umidade | vento | jogar | |
|---|---|---|---|---|---|
| 90 | 30 | 85 | 30 | 0 | |
| 80 | 21 | 80 | 12 | 0 | |
| 40 | 20 | 75 | 7 | 1 | |
| 30 | 18 | 70 | 8 | 0 | |
| 20 | 32 | 60 | 8 | 1 | |
| 60 | 34 | 60 | 15 | 1 |
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Learn more about bidirectional Unicode characters
| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "Sub - Inteligência Computacional.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [] | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "yWRGaEUvlrTz" | |
| }, | |
| "source": [ | |
| "# Sub - Inteligência Computacional\n", | |
| "\n", | |
| "Vinícius Lourenço Claro Cardoso - 180618" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "CqhefAQolsm6" | |
| }, | |
| "source": [ | |
| "# KNN" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "pP_VVmmtcbJB" | |
| }, | |
| "source": [ | |
| "#Leitura dos módulos\n", | |
| "from __future__ import print_function\n", | |
| "\n", | |
| "import pandas as pd\n", | |
| "from pandas import Series,DataFrame\n", | |
| "\n", | |
| "from sklearn import svm\n", | |
| "from sklearn.model_selection import train_test_split\n", | |
| "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n", | |
| "\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "np.random.seed(1000)\n", | |
| "\n", | |
| "import seaborn as sns\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "import keras\n", | |
| "from keras.datasets import fashion_mnist\n", | |
| "from keras.models import Sequential\n", | |
| "from keras.layers import Dense, Dropout\n", | |
| "from keras.optimizers import RMSprop\n", | |
| "from keras.optimizers import Adam\n", | |
| "\n", | |
| "from sklearn.neighbors import KNeighborsClassifier" | |
| ], | |
| "execution_count": 74, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "tau7PnaccZmj" | |
| }, | |
| "source": [ | |
| "data_set = pd.read_csv(\"https://gist.githubusercontent.com/H4ad/e4c5f79849eebdec32b655d7bba6be0a/raw/c721e3a88157743576f9ade9f3cfa15ea86164e0/sub.csv\",sep=\",\",header=0)" | |
| ], | |
| "execution_count": 75, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 484 | |
| }, | |
| "id": "e9FwpNRdcz0B", | |
| "outputId": "c8c5021e-eadd-424f-ec3c-996c860fab72" | |
| }, | |
| "source": [ | |
| "display(data_set.head(n=5))\n", | |
| "display(data_set.describe())" | |
| ], | |
| "execution_count": 76, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "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>a1</th>\n", | |
| " <th>a2</th>\n", | |
| " <th>a3</th>\n", | |
| " <th>a4</th>\n", | |
| " <th>classe</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>9</td>\n", | |
| " <td>A</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>3</td>\n", | |
| " <td>0</td>\n", | |
| " <td>B</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>4</td>\n", | |
| " <td>9</td>\n", | |
| " <td>C</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>2</td>\n", | |
| " <td>0</td>\n", | |
| " <td>B</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>1</td>\n", | |
| " <td>2</td>\n", | |
| " <td>3</td>\n", | |
| " <td>9</td>\n", | |
| " <td>C</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " a1 a2 a3 a4 classe\n", | |
| "0 1 0 2 9 A\n", | |
| "1 0 1 3 0 B\n", | |
| "2 1 1 4 9 C\n", | |
| "3 0 2 2 0 B\n", | |
| "4 1 2 3 9 C" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "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>a1</th>\n", | |
| " <th>a2</th>\n", | |
| " <th>a3</th>\n", | |
| " <th>a4</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>5.00000</td>\n", | |
| " <td>5.00000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>0.600000</td>\n", | |
| " <td>1.20000</td>\n", | |
| " <td>2.80000</td>\n", | |
| " <td>5.400000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>0.547723</td>\n", | |
| " <td>0.83666</td>\n", | |
| " <td>0.83666</td>\n", | |
| " <td>4.929503</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.00000</td>\n", | |
| " <td>2.00000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.00000</td>\n", | |
| " <td>2.00000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.00000</td>\n", | |
| " <td>3.00000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>2.00000</td>\n", | |
| " <td>3.00000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>2.00000</td>\n", | |
| " <td>4.00000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " a1 a2 a3 a4\n", | |
| "count 5.000000 5.00000 5.00000 5.000000\n", | |
| "mean 0.600000 1.20000 2.80000 5.400000\n", | |
| "std 0.547723 0.83666 0.83666 4.929503\n", | |
| "min 0.000000 0.00000 2.00000 0.000000\n", | |
| "25% 0.000000 1.00000 2.00000 0.000000\n", | |
| "50% 1.000000 1.00000 3.00000 9.000000\n", | |
| "75% 1.000000 2.00000 3.00000 9.000000\n", | |
| "max 1.000000 2.00000 4.00000 9.000000" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "UNMRXadccTzs" | |
| }, | |
| "source": [ | |
| "x = DataFrame(data_set.drop(\"classe\",axis=1))\n", | |
| "y = DataFrame(data_set[\"classe\"])" | |
| ], | |
| "execution_count": 77, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "dbjY7Rjfb1a1", | |
| "outputId": "61856671-6c52-4237-e5db-69654fd72411" | |
| }, | |
| "source": [ | |
| "knn = KNeighborsClassifier(n_neighbors=2)\n", | |
| "knn.fit(x, y)\n", | |
| "# Como há pouco dado, eu basicamente pulei a parte de dados de treino e teste,\n", | |
| "# e passei direto para testar com o valor que foi pedido na sub." | |
| ], | |
| "execution_count": 78, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", | |
| " \n" | |
| ], | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", | |
| " metric_params=None, n_jobs=None, n_neighbors=2, p=2,\n", | |
| " weights='uniform')" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 78 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "eVWDEoCheye8", | |
| "outputId": "db3dd290-47ef-46e2-bd0c-9c63664cfb3d" | |
| }, | |
| "source": [ | |
| "# Cria um dataset para a sub\n", | |
| "x_sub_test = [[1,2.5,3,8]]\n", | |
| "sub_dataset = pd.DataFrame(data, columns = ['a1', 'a2', 'a3', 'a4'])\n", | |
| "\n", | |
| "print(f\"A classe para os valores x = [1 2,5 3 8]: {knn.predict(df)}\\n\")\n" | |
| ], | |
| "execution_count": 79, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "A classe para os valores x = [1 2,5 3 8]: ['C']\n", | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "pBcqGYRPluEB" | |
| }, | |
| "source": [ | |
| "# Naive Bayes" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 204 | |
| }, | |
| "id": "6N90QdYwhnVJ", | |
| "outputId": "fd9eb6f8-a833-40d0-88d7-2bac91967669" | |
| }, | |
| "source": [ | |
| "bayes_set = pd.read_csv(\"https://gist.githubusercontent.com/H4ad/e4c5f79849eebdec32b655d7bba6be0a/raw/822964d28ce98bcc555f50ee0f0626b4b5409b8a/bayes_data.csv\",sep=\",\",header=0)\n", | |
| "\n", | |
| "bayes_set.head(5)" | |
| ], | |
| "execution_count": 80, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>precipitacao</th>\n", | |
| " <th>temperatura</th>\n", | |
| " <th>umidade</th>\n", | |
| " <th>vento</th>\n", | |
| " <th>jogar</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>90</td>\n", | |
| " <td>30</td>\n", | |
| " <td>85</td>\n", | |
| " <td>30</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>80</td>\n", | |
| " <td>21</td>\n", | |
| " <td>80</td>\n", | |
| " <td>12</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>40</td>\n", | |
| " <td>20</td>\n", | |
| " <td>75</td>\n", | |
| " <td>7</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>30</td>\n", | |
| " <td>18</td>\n", | |
| " <td>70</td>\n", | |
| " <td>8</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>20</td>\n", | |
| " <td>32</td>\n", | |
| " <td>60</td>\n", | |
| " <td>8</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " precipitacao temperatura umidade vento jogar\n", | |
| "0 90 30 85 30 0\n", | |
| "1 80 21 80 12 0\n", | |
| "2 40 20 75 7 1\n", | |
| "3 30 18 70 8 0\n", | |
| "4 20 32 60 8 1" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 80 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "LnnUglOMh97I" | |
| }, | |
| "source": [ | |
| "xBayes = DataFrame(bayes_set.drop(\"jogar\",axis=1))\n", | |
| "yBayes = DataFrame(bayes_set[\"jogar\"])" | |
| ], | |
| "execution_count": 81, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Wdo19GXGh7T9", | |
| "outputId": "5d2d8d9b-d710-4614-e549-e7067606c423" | |
| }, | |
| "source": [ | |
| "from sklearn.naive_bayes import GaussianNB\n", | |
| "from sklearn.naive_bayes import MultinomialNB\n", | |
| "\n", | |
| "modelGaussian = GaussianNB()\n", | |
| "modelGaussian.fit(xBayes, yBayes)\n", | |
| "\n", | |
| "modelMultinomial = MultinomialNB()\n", | |
| "modelMultinomial.fit(xBayes, yBayes)" | |
| ], | |
| "execution_count": 82, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/usr/local/lib/python3.7/dist-packages/sklearn/naive_bayes.py:206: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", | |
| " y = column_or_1d(y, warn=True)\n", | |
| "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", | |
| " y = column_or_1d(y, warn=True)\n" | |
| ], | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 82 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "4OKSKJpRiIPQ", | |
| "outputId": "963d02a3-ae94-4092-b558-130d3ff5ad23" | |
| }, | |
| "source": [ | |
| "bayesTest = [[70, 20, 75, 16]]\n", | |
| "\n", | |
| "gaussianBayesPred = modelGaussian.predict(bayesTest)\n", | |
| "multinomialBayesPred = modelMultinomial.predict(bayesTest)\n", | |
| "\n", | |
| "print(\"0 = não, 1 = sim\\n\")\n", | |
| "print(f\"[Gaussian] A probabilidade para os valores x = [70, 20, 75, 16]: {gaussianBayesPred}\")\n", | |
| "print(f\"[Multinomial] A probabilidade para os valores x = [70, 20, 75, 16]: {multinomialBayesPred}\")" | |
| ], | |
| "execution_count": 83, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "0 = não, 1 = sim\n", | |
| "\n", | |
| "[Gaussian] A probabilidade para os valores x = [70, 20, 75, 16]: [0]\n", | |
| "[Multinomial] A probabilidade para os valores x = [70, 20, 75, 16]: [0]\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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| a1 | a2 | a3 | a4 | classe | |
|---|---|---|---|---|---|
| 1 | 0 | 2 | 9 | A | |
| 0 | 1 | 3 | 0 | B | |
| 1 | 1 | 4 | 9 | C | |
| 0 | 2 | 2 | 0 | B | |
| 1 | 2 | 3 | 9 | C |
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