preditor_de_gols.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projeto de Deep Learning\n",
    "### 2021/2\n",
    "\n",
    "Predição da quantidade de gols em partidas de futebol do campeonato brasileiro com base em dados históricos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lendo base de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>mandante</th>\n",
       "      <th>visitante</th>\n",
       "      <th>mandante_placar</th>\n",
       "      <th>visitante_placar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Guarani</td>\n",
       "      <td>Vasco</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Athletico-PR</td>\n",
       "      <td>Gremio</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flamengo</td>\n",
       "      <td>Coritiba</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Goias</td>\n",
       "      <td>Paysandu</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Internacional</td>\n",
       "      <td>Ponte Preta</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mandante    visitante  mandante_placar  visitante_placar\n",
       "0        Guarani        Vasco                4                 2\n",
       "1   Athletico-PR       Gremio                2                 0\n",
       "2       Flamengo     Coritiba                1                 1\n",
       "3          Goias     Paysandu                2                 2\n",
       "4  Internacional  Ponte Preta                1                 1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"campeonato-brasileiro-full.csv\")[[\"mandante\", \"visitante\", \"mandante_placar\", \"visitante_placar\"]].dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecionando as features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Guarani', 'Vasco'],\n",
       "       ['Athletico-PR', 'Gremio'],\n",
       "       ['Flamengo', 'Coritiba'],\n",
       "       ...,\n",
       "       ['Juventude', 'Corinthians'],\n",
       "       ['Gremio', 'Atletico-MG'],\n",
       "       ['Santos', 'Cuiaba']], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:, :2].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "print(max(y))\n",
    "print(min(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tratando target do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "0\n"
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    },
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       " [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
       " [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " ...]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = [i[0] + i[1] for i in df.iloc[:, 2:].values]\n",
    "print(max(y))\n",
    "print(min(y))\n",
    "\n",
    "def set_y(i):\n",
    "    alist = [0]*12\n",
    "    alist[int(i)] = 1\n",
    "    return alist\n",
    "\n",
    "y = list(map(set_y, y))\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tratando variáveis categóricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "X[:, 0] = le.fit_transform(X[:, 0])\n",
    "X[:, 1] = le.fit_transform(X[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dividindo dados em treino e teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Montando modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-30 01:05:30.833389: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-04-30 01:05:30.833412: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2022-04-30 01:05:32.202672: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-30 01:05:32.202977: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-04-30 01:05:32.203047: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory\n",
      "2022-04-30 01:05:32.203111: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory\n",
      "2022-04-30 01:05:32.203169: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory\n",
      "2022-04-30 01:05:32.203222: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory\n",
      "2022-04-30 01:05:32.203286: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory\n",
      "2022-04-30 01:05:32.203334: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory\n",
      "2022-04-30 01:05:32.203380: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory\n",
      "2022-04-30 01:05:32.203387: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2022-04-30 01:05:32.203611: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "ann = tf.keras.models.Sequential()\n",
    "ann.add(tf.keras.layers.Dense(units=12, activation='tanh'))\n",
    "ann.add(tf.keras.layers.Dense(units=12, activation='tanh'))\n",
    "ann.add(tf.keras.layers.Dense(units=12, activation='softmax'))\n",
    "ann.compile(optimizer = tf.keras.optimizers.SGD() , loss = 'categorical_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Treinando modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "192/192 [==============================] - 1s 1ms/step - loss: 2.1288 - accuracy: 0.2181\n",
      "Epoch 2/15\n",
      "192/192 [==============================] - 0s 888us/step - loss: 1.9520 - accuracy: 0.2484\n",
      "Epoch 3/15\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.9169 - accuracy: 0.2487\n",
      "Epoch 4/15\n",
      "192/192 [==============================] - 0s 866us/step - loss: 1.9022 - accuracy: 0.2474\n",
      "Epoch 5/15\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.8938 - accuracy: 0.2477\n",
      "Epoch 6/15\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.8884 - accuracy: 0.2487\n",
      "Epoch 7/15\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.8847 - accuracy: 0.2482\n",
      "Epoch 8/15\n",
      "192/192 [==============================] - 0s 932us/step - loss: 1.8819 - accuracy: 0.2482\n",
      "Epoch 9/15\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.8803 - accuracy: 0.2479\n",
      "Epoch 10/15\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.8785 - accuracy: 0.2482\n",
      "Epoch 11/15\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.8772 - accuracy: 0.2482\n",
      "Epoch 12/15\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.8763 - accuracy: 0.2479\n",
      "Epoch 13/15\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.8754 - accuracy: 0.2474\n",
      "Epoch 14/15\n",
      "192/192 [==============================] - 0s 998us/step - loss: 1.8746 - accuracy: 0.2477\n",
      "Epoch 15/15\n",
      "192/192 [==============================] - 0s 1ms/step - loss: 1.8741 - accuracy: 0.2485\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f6874265150>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convertendo dados para o tensorflow\n",
    "X_train = np.asarray(X_train).astype(np.float32)\n",
    "y_train = np.asarray(y_train).astype(np.float32)\n",
    "\n",
    "ann.fit(X_train, y_train, batch_size = 32, epochs = 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predizendo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08047223, 0.197794  , 0.24323823, ..., 0.00355341, 0.00371274,\n",
       "        0.00179912],\n",
       "       [0.08047247, 0.19779375, 0.24323833, ..., 0.00355341, 0.00371274,\n",
       "        0.00179912],\n",
       "       [0.07725565, 0.19738857, 0.26093215, ..., 0.00226372, 0.00306727,\n",
       "        0.00332351],\n",
       "       ...,\n",
       "       [0.08037212, 0.1980072 , 0.24306588, ..., 0.00357196, 0.00371565,\n",
       "        0.00181421],\n",
       "       [0.07735338, 0.19471376, 0.25764975, ..., 0.00227451, 0.0030742 ,\n",
       "        0.00347362],\n",
       "       [0.08159273, 0.19789258, 0.25740537, ..., 0.00233345, 0.00312972,\n",
       "        0.0029468 ]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = np.asarray(X_test).astype(np.float32)\n",
    "\n",
    "y_pred = ann.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_y(i):\n",
    "    return list(i).index(max(i))\n",
    "\n",
    "predicao = list(map(get_y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusão\n",
    "\n",
    "Devido a lógica do esporte observamos que o modelo apresenta problemas de viés por conta de ser mais comum que os jogos de futebol tenham por volta de 1 a 3 gols por partida."
   ]
  }
 ],
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