From 4d41adcca37259361c988d2729f7bc7e59013112 Mon Sep 17 00:00:00 2001
From: felipe gibin <gibinf.duarte@gmail.com>
Date: Sat, 1 Nov 2025 19:32:36 -0300
Subject: [PATCH 1/4] feat(text encoder): cria modelos do encoder e decoder de
 texto

---
 models/Text_Encoder.ipynb | 357 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 357 insertions(+)
 create mode 100644 models/Text_Encoder.ipynb

diff --git a/models/Text_Encoder.ipynb b/models/Text_Encoder.ipynb
new file mode 100644
index 0000000..1659a68
--- /dev/null
+++ b/models/Text_Encoder.ipynb
@@ -0,0 +1,357 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Implementação do Encoder e Decoder Determinístico de Ação\n",
+        "\n",
+        "\n",
+        "Seguindo o modelo conceitual definido pela arquitetura, em que o World Model opera com pares instantâneos `(St​,at​)`, este notebook irá construir e testar a arquitetura básica de codificação de uma única ação em um instante de tempo `t`. O modelo do enconder traduz o objeto JSON que representa a ação da simulação em um vetor latente compacto `za` e o decoder o reconstrói, a fim de testar a corretude do encoder.\n",
+        "\n",
+        "Cada ação tem:\n",
+        "- `time`: timestamp em segundos\n",
+        "- `type`: tipo de ação (mouse_down)\n",
+        "- `object`: objeto afetado (ball)\n",
+        "- `pos`: [x, y] coordenadas da ação\n",
+        "\n",
+        "Note que o encoder não codifica o campo `time`, assume-se que a ação e o frame estarão casados temporalmente na etapa do WFM"
+      ],
+      "metadata": {
+        "id": "hxYwMDg5G7Oz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Dependências"
+      ],
+      "metadata": {
+        "id": "ZIdMiQqrG_H6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "Sdx_icDB2Vdb",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1101b53d-57d4-49cb-c053-11fe3ac71d0e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<torch._C.Generator at 0x79124803ceb0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ],
+      "source": [
+        "# Importações necessárias\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import numpy as np\n",
+        "import json\n",
+        "import random\n",
+        "import torch.optim as optim\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "\n",
+        "# Garante a reprodutibilidade\n",
+        "torch.manual_seed(42)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Definições do Cenário e Codificação\n",
+        "\n",
+        "A primeira etapa consiste em codificar uma ação em um tensor do Pytorch. A ausência de ação corresponde à um tensor nulo."
+      ],
+      "metadata": {
+        "id": "cdpGn7BkHDxr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Dimensões da tela do cenário 1\n",
+        "SCREEN_WIDTH = 800\n",
+        "SCREEN_HEIGHT = 600\n",
+        "\n",
+        "# Codificação One-Hot\n",
+        "TYPE_ENCODING = {\n",
+        "    'mouse_down': [1.0],  # Ação presente\n",
+        "    'none':  [0.0]        # Ação ausente\n",
+        "}\n",
+        "\n",
+        "OBJECT_ENCODING = {\n",
+        "    'ball': [1.0], # Cenário 1 só tem bolas\n",
+        "    'none': [0.0]  # Falta de ação, 0 para que o vetor final da falta de ação seja nulo\n",
+        "}\n",
+        "\n",
+        "# Dimensão do vetor de entrada: [type(1)] + [object(1)] + [x_norm(1)] + [y_norm(1)]\n",
+        "INPUT_VECTOR_DIM = 4\n",
+        "\n",
+        "LATENT_ACTION_DIM = 16 # Za: Dimensão do Espaço Latente da Ação"
+      ],
+      "metadata": {
+        "id": "8GiQ-3UBIFqz"
+      },
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Função de codificação\n",
+        "def encoding_function(action_data: dict = None) -> torch.Tensor:\n",
+        "  \"\"\"\n",
+        "    Converte um dicionário de ação ou um sinal de 'ausência de ação' em um tensor\n",
+        "\n",
+        "    Args:\n",
+        "        action_data: Dicionário de ação (JSON) ou None na ausência dela\n",
+        "\n",
+        "    Returns:\n",
+        "        Um tensor PyTorch de dimensão [INPUT_VECTOR_DIM]\n",
+        "  \"\"\"\n",
+        "\n",
+        "  # Caso de ausencia de ação\n",
+        "  if action_data is None:\n",
+        "    type_vector = TYPE_ENCODING['none']\n",
+        "    object_vector = OBJECT_ENCODING['none']\n",
+        "    x_norm = 0.0\n",
+        "    y_norm = 0.0\n",
+        "\n",
+        "  # Ação positiva (mouse_down)\n",
+        "  elif action_data:\n",
+        "    type_vector = TYPE_ENCODING['mouse_down']\n",
+        "    object_vector = OBJECT_ENCODING.get(action_data['object'], [0.0]) # Pega o vetor da ball\n",
+        "    pos_x = action_data['pos'][0]\n",
+        "    pos_y = action_data['pos'][1]\n",
+        "\n",
+        "    # Normaliza as coordenadas da tela para o intervalo [0, 1]\n",
+        "    x_norm = pos_x / SCREEN_WIDTH\n",
+        "    y_norm = pos_y / SCREEN_HEIGHT\n",
+        "\n",
+        "  input_list = type_vector + object_vector + [x_norm, y_norm]\n",
+        "\n",
+        "  if len(input_list) != INPUT_VECTOR_DIM:\n",
+        "    raise ValueError(f\"Dimensão do vetor inválida: {len(input_list)}\")\n",
+        "\n",
+        "  return torch.tensor(input_list, dtype=torch.float32)"
+      ],
+      "metadata": {
+        "id": "WyiQULmkJKEz"
+      },
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Exemplo de ação mouse_down\n",
+        "exemplo_acao = {\n",
+        "    \"time\": 9.40,\n",
+        "    \"type\": \"mouse_down\",\n",
+        "    \"object\": \"ball\",\n",
+        "    \"pos\": [400, 300]\n",
+        "}\n",
+        "\n",
+        "# Testando codificação de ação presente\n",
+        "vetor_acao = encoding_function(exemplo_acao)\n",
+        "print(\"Ação presente (mouse_down):\", vetor_acao.tolist())\n",
+        "\n",
+        "# Testando codificação de ausência de ação (No-Op)\n",
+        "vetor_none = encoding_function(None)\n",
+        "print(\"Ausência de ação:\", vetor_none.tolist())\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bTmDJytpTMzP",
+        "outputId": "8fd024c9-6519-4d92-d98e-7ba5b3e3b4a1"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Ação presente (mouse_down): [1.0, 1.0, 0.5, 0.5]\n",
+            "Ausência de ação: [0.0, 0.0, 0.0, 0.0]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Criando o Encoder e Decoder\n",
+        "\n",
+        "Agora vamos criar uma rede neural simples que transforma esses 4 features em um embedding mais rico. Depois, vamos fazer o mesmo só que para o movimento contrário. Optou-se por uma rede simples inicialmente, para fins de teste."
+      ],
+      "metadata": {
+        "id": "dUEH1ACJV0Rz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class TextEncoder(nn.Module):\n",
+        "  \"\"\"\n",
+        "  Encoder determinístico (embedding)\n",
+        "  Mapeia o vetor de entrada para o espaço latente de 16 dimensões\n",
+        "  \"\"\"\n",
+        "\n",
+        "  def __init__(self, input_dim=INPUT_VECTOR_DIM, latent_dim=LATENT_ACTION_DIM):\n",
+        "    super().__init__()\n",
+        "\n",
+        "    self.latent_dim = latent_dim\n",
+        "\n",
+        "    self.net = nn.Sequential(\n",
+        "      nn.Linear(input_dim, 16),\n",
+        "      nn.ReLU(),\n",
+        "      nn.Linear(16, latent_dim)\n",
+        "    )\n",
+        "\n",
+        "  def forward(self, x):\n",
+        "    return self.net(x)"
+      ],
+      "metadata": {
+        "id": "skhXNuOlP1uw"
+      },
+      "execution_count": 23,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class TextDecoder(nn.Module):\n",
+        "  \"\"\"\n",
+        "  Decoder determinístico (reconstrução)\n",
+        "  Mapeia o vetor latente de volta para o espaço da ação original\n",
+        "  \"\"\"\n",
+        "\n",
+        "  def __init__(self, latent_dim=LATENT_ACTION_DIM, output_dim=INPUT_VECTOR_DIM):\n",
+        "    super().__init__()\n",
+        "\n",
+        "    self.net = nn.Sequential(\n",
+        "      nn.Linear(latent_dim, 16),\n",
+        "      nn.ReLU(),\n",
+        "      nn.Linear(16, output_dim)\n",
+        "    )\n",
+        "\n",
+        "  def forward(self, z):\n",
+        "    return self.net(z)"
+      ],
+      "metadata": {
+        "id": "tio4iMccYTiB"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Geração de um dataset sintético simples\n",
+        "\n",
+        "Como a estrutura das ações é muito simples, é mais vantajoso montar um dataset sintético com mais ações do que usar o dataset real com 200 simulações. Note que na simulação real, menos de 1% dos frames terão uma ação correspondente, logo a proporção de ações positivas (mouse_click) em relação a quantidade de frames é de 1%, a fim de espelhar o comportamento real da simulação."
+      ],
+      "metadata": {
+        "id": "qBlYqqB9cBYA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "NUM_SAMPLES = 2000     # Número de amostras sintéticas\n",
+        "BATCH_SIZE = 64        # Tamanho do lote\n",
+        "\n",
+        "# Lista para armazenar os vetores de input\n",
+        "synthetic_inputs = []\n",
+        "\n",
+        "# Gerar amostras de clique\n",
+        "for _ in range(20): # 1% de 2000\n",
+        "\n",
+        "  pos_x = random.randint(50, 750)\n",
+        "  pos_y = random.randint(50, 500)\n",
+        "\n",
+        "  # Monta a estrutura de dados\n",
+        "  action_data = {\n",
+        "    \"type\": \"mouse_down\",\n",
+        "    \"object\": \"ball\",\n",
+        "    \"pos\": [pos_x, pos_y]\n",
+        "  }\n",
+        "\n",
+        "  # Codifica e armazena\n",
+        "  synthetic_inputs.append(encoding_function(action_data))\n",
+        "\n",
+        "\n",
+        "# Gerar amostras de Ausência de Ação\n",
+        "for _ in range(1980): # 99% de 2000\n",
+        "  # Quando não tem ação, passa um None à função de encoding\n",
+        "  synthetic_inputs.append(encoding_function(None))"
+      ],
+      "metadata": {
+        "id": "ORoCvrBVceZp"
+      },
+      "execution_count": 32,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Teste de entradas da lista de ações sintéticas\n",
+        "print(synthetic_inputs[0])\n",
+        "print(synthetic_inputs[50])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z5BBywwygXJr",
+        "outputId": "dc4ed2b3-6048-4047-f471-59e82648fb58"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([1.0000, 1.0000, 0.2175, 0.7183])\n",
+            "tensor([0., 0., 0., 0.])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 5."
+      ],
+      "metadata": {
+        "id": "U1CNwmw9eGeo"
+      }
+    }
+  ]
+}
\ No newline at end of file

From 54cbee53f5d3ac7d2e78bf6eb4e8760a76bd2bf1 Mon Sep 17 00:00:00 2001
From: felipe gibin <gibinf.duarte@gmail.com>
Date: Sun, 2 Nov 2025 18:24:03 -0300
Subject: [PATCH 2/4] =?UTF-8?q?feat:=20adiciona=20se=C3=A7=C3=A3o=20de=20t?=
 =?UTF-8?q?reinamento=20do=20encoder-decoder?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

fix: muda o split de dados do dataset sintetico para 50% dados do tipo mouse_click e 50% dados sem nenhuma ação
---
 models/Text_Encoder.ipynb | 159 +++++++++++++++++++++++++++++++-------
 1 file changed, 132 insertions(+), 27 deletions(-)

diff --git a/models/Text_Encoder.ipynb b/models/Text_Encoder.ipynb
index 1659a68..e5a55fa 100644
--- a/models/Text_Encoder.ipynb
+++ b/models/Text_Encoder.ipynb
@@ -45,24 +45,24 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": 58,
       "metadata": {
         "id": "Sdx_icDB2Vdb",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "1101b53d-57d4-49cb-c053-11fe3ac71d0e"
+        "outputId": "4f90b044-33e0-4700-94ee-dcf22aecf545"
       },
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "<torch._C.Generator at 0x79124803ceb0>"
+              "<torch._C.Generator at 0x79339d090d30>"
             ]
           },
           "metadata": {},
-          "execution_count": 26
+          "execution_count": 58
         }
       ],
       "source": [
@@ -74,6 +74,7 @@
         "import random\n",
         "import torch.optim as optim\n",
         "from torch.utils.data import TensorDataset, DataLoader\n",
+        "import matplotlib.pyplot as plt\n",
         "\n",
         "# Garante a reprodutibilidade\n",
         "torch.manual_seed(42)"
@@ -116,7 +117,7 @@
       "metadata": {
         "id": "8GiQ-3UBIFqz"
       },
-      "execution_count": 20,
+      "execution_count": 59,
       "outputs": []
     },
     {
@@ -162,7 +163,7 @@
       "metadata": {
         "id": "WyiQULmkJKEz"
       },
-      "execution_count": 21,
+      "execution_count": 60,
       "outputs": []
     },
     {
@@ -189,9 +190,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "bTmDJytpTMzP",
-        "outputId": "8fd024c9-6519-4d92-d98e-7ba5b3e3b4a1"
+        "outputId": "6c4597da-8721-48d2-aa90-5afda4ff5b02"
       },
-      "execution_count": 22,
+      "execution_count": 61,
       "outputs": [
         {
           "output_type": "stream",
@@ -240,7 +241,7 @@
       "metadata": {
         "id": "skhXNuOlP1uw"
       },
-      "execution_count": 23,
+      "execution_count": 62,
       "outputs": []
     },
     {
@@ -262,12 +263,34 @@
         "    )\n",
         "\n",
         "  def forward(self, z):\n",
-        "    return self.net(z)"
+        "    x = self.net(z)\n",
+        "    return torch.sigmoid(x)\n",
+        "\n",
+        "\n",
+        "class TextDecoder(nn.Module):\n",
+        "  \"\"\"\n",
+        "  Decoder determinístico (reconstrução)\n",
+        "  Mapeia o vetor latente de volta para o espaço da ação original\n",
+        "  \"\"\"\n",
+        "\n",
+        "  def __init__(self, latent_dim=LATENT_ACTION_DIM, output_dim=INPUT_VECTOR_DIM):\n",
+        "    super().__init__()\n",
+        "\n",
+        "    self.net = nn.Sequential(\n",
+        "      nn.Linear(latent_dim, 32),\n",
+        "      nn.ReLU(),\n",
+        "      nn.Linear(32, 16),\n",
+        "      nn.ReLU(),\n",
+        "      nn.Linear(16, output_dim)\n",
+        "    )\n",
+        "\n",
+        "  def forward(self, z):\n",
+        "    return self.net(z)\n"
       ],
       "metadata": {
         "id": "tio4iMccYTiB"
       },
-      "execution_count": 24,
+      "execution_count": 63,
       "outputs": []
     },
     {
@@ -275,7 +298,7 @@
       "source": [
         "## 4. Geração de um dataset sintético simples\n",
         "\n",
-        "Como a estrutura das ações é muito simples, é mais vantajoso montar um dataset sintético com mais ações do que usar o dataset real com 200 simulações. Note que na simulação real, menos de 1% dos frames terão uma ação correspondente, logo a proporção de ações positivas (mouse_click) em relação a quantidade de frames é de 1%, a fim de espelhar o comportamento real da simulação."
+        "Como a estrutura das ações é muito simples, é mais vantajoso montar um dataset sintético com mais ações do que usar o dataset real com as 200 simulações. Note que na simulação real, menos de 1% dos frames terão uma ação correspondente. Mesmo assim, opta-se por fazer um split de 50/50 em dados com ação positiva e nula. Isso se deve principalmente porque caso a divisão que espelha o comportamento real fosse usada, o modelo não teria incentivos para minimizar a perda das ações com clique, e focaria apenas em minimizar ao máximo o erro da ausência de ação, ignorando o outro caso."
       ],
       "metadata": {
         "id": "qBlYqqB9cBYA"
@@ -284,14 +307,13 @@
     {
       "cell_type": "code",
       "source": [
-        "NUM_SAMPLES = 2000     # Número de amostras sintéticas\n",
-        "BATCH_SIZE = 64        # Tamanho do lote\n",
+        "NUM_SAMPLES = 2000\n",
+        "BATCH_SIZE = 64\n",
         "\n",
-        "# Lista para armazenar os vetores de input\n",
         "synthetic_inputs = []\n",
         "\n",
         "# Gerar amostras de clique\n",
-        "for _ in range(20): # 1% de 2000\n",
+        "for _ in range(1000): # 50% de 2000\n",
         "\n",
         "  pos_x = random.randint(50, 750)\n",
         "  pos_y = random.randint(50, 500)\n",
@@ -308,38 +330,43 @@
         "\n",
         "\n",
         "# Gerar amostras de Ausência de Ação\n",
-        "for _ in range(1980): # 99% de 2000\n",
+        "for _ in range(1000): # 50% de 2000\n",
         "  # Quando não tem ação, passa um None à função de encoding\n",
         "  synthetic_inputs.append(encoding_function(None))"
       ],
       "metadata": {
         "id": "ORoCvrBVceZp"
       },
-      "execution_count": 32,
+      "execution_count": 64,
       "outputs": []
     },
     {
       "cell_type": "code",
       "source": [
-        "# Teste de entradas da lista de ações sintéticas\n",
-        "print(synthetic_inputs[0])\n",
-        "print(synthetic_inputs[50])"
+        "full_tensor = torch.stack(synthetic_inputs)\n",
+        "\n",
+        "# Note que como queremos reconstruir a entrada, ela é a mesma coisa que a saída\n",
+        "dataset = TensorDataset(full_tensor, full_tensor)\n",
+        "dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
+        "\n",
+        "print(f\"Dataset Sintético Gerado: {len(full_tensor)} amostras.\")\n",
+        "print(f\"Shape de um Batch: {next(iter(dataloader))[0].shape}\")"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "Z5BBywwygXJr",
-        "outputId": "dc4ed2b3-6048-4047-f471-59e82648fb58"
+        "id": "7r3PyF72R1LK",
+        "outputId": "cc42ad78-3769-48d7-ca5f-58babd794bf9"
       },
-      "execution_count": 35,
+      "execution_count": 65,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "tensor([1.0000, 1.0000, 0.2175, 0.7183])\n",
-            "tensor([0., 0., 0., 0.])\n"
+            "Dataset Sintético Gerado: 2000 amostras.\n",
+            "Shape de um Batch: torch.Size([64, 4])\n"
           ]
         }
       ]
@@ -347,11 +374,89 @@
     {
       "cell_type": "markdown",
       "source": [
-        "## 5."
+        "## 5. Treinamento"
       ],
       "metadata": {
         "id": "U1CNwmw9eGeo"
       }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Iniciação dos modelos e otimizador\n",
+        "text_encoder = TextEncoder()\n",
+        "text_decoder = TextDecoder()\n",
+        "\n",
+        "# Vamos combinar os parâmetros dos dois modelos em uma única lista de parâmetros e otimizá-los de uma vez\n",
+        "params = list(text_encoder.parameters()) + list(text_decoder.parameters())\n",
+        "optimizer = optim.Adam(params, lr=1e-3)\n",
+        "\n",
+        "# Função de Perda é a MSE\n",
+        "loss_function = nn.MSELoss()"
+      ],
+      "metadata": {
+        "id": "j2N-9JrVTVBm"
+      },
+      "execution_count": 66,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "NUM_EPOCHS = 20\n",
+        "print(\"Iniciando treinamento\")\n",
+        "\n",
+        "train_losses = [] # Perdas de treinamento para plotar depois\n",
+        "\n",
+        "for epoch in range(NUM_EPOCHS):\n",
+        "    total_loss = 0  # Acumula a perda total de cada época\n",
+        "\n",
+        "    # Loop dos batches\n",
+        "    for data, _ in dataloader:\n",
+        "        optimizer.zero_grad()\n",
+        "\n",
+        "        # Forward pass, codifica na representação latente\n",
+        "        z_a = text_encoder(data)\n",
+        "\n",
+        "        # Forward pass, decodifica a representação latente\n",
+        "        reconstruction = text_decoder(z_a)\n",
+        "\n",
+        "        # Calcula a perda entre a reconstrução e os dados originais\n",
+        "        loss = loss_function(reconstruction, data)\n",
+        "\n",
+        "        loss.backward()  # Backpropagation\n",
+        "        optimizer.step()  # Atualiza os pesos do modelo com base nos gradientes\n",
+        "\n",
+        "        total_loss += loss.item()\n",
+        "\n",
+        "    avg_loss = total_loss / len(dataloader)\n",
+        "    train_losses.append(avg_loss)\n",
+        "\n",
+        "    # Mostra a perda a cada 5 épocas\n",
+        "    if (epoch + 1) % 5 == 0:\n",
+        "        print(f\"Epoch [{epoch+1}/{NUM_EPOCHS}], Loss de Reconstrução: {avg_loss:.6f}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BCrX5bDwUOau",
+        "outputId": "f3af6d83-f596-458b-971a-a42a7bbfc762"
+      },
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Iniciando treinamento\n",
+            "Epoch [5/20], Loss de Reconstrução: 0.013939\n",
+            "Epoch [10/20], Loss de Reconstrução: 0.010992\n",
+            "Epoch [15/20], Loss de Reconstrução: 0.008542\n",
+            "Epoch [20/20], Loss de Reconstrução: 0.003926\n"
+          ]
+        }
+      ]
     }
   ]
 }
\ No newline at end of file

From 89a3c3e592665d664002d654057c1817cf6aa291 Mon Sep 17 00:00:00 2001
From: yuri-sl <yuri03ysl@gmail.com>
Date: Mon, 3 Nov 2025 20:45:37 -0300
Subject: [PATCH 3/4] =?UTF-8?q?feat:=20adi=C3=A7=C3=A3o=20de=20valida?=
 =?UTF-8?q?=C3=A7=C3=A3o=20de=20treinamento=20e=20plotagem=20de=20gr=C3=A1?=
 =?UTF-8?q?fico=20com=20os=20resultados=20do=20treinamento=20e=20valida?=
 =?UTF-8?q?=C3=A7=C3=A3o=20+=20adi=C3=A7=C3=A3o=20de=20features=20extras?=
 =?UTF-8?q?=20quanto=20a=20relatorios=20de=20erros?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 models/Text_Encoder.ipynb | 475 ++++++++++++++++++++++++++++----------
 1 file changed, 348 insertions(+), 127 deletions(-)

diff --git a/models/Text_Encoder.ipynb b/models/Text_Encoder.ipynb
index e5a55fa..1d95a5f 100644
--- a/models/Text_Encoder.ipynb
+++ b/models/Text_Encoder.ipynb
@@ -1,21 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "hxYwMDg5G7Oz"
+      },
       "source": [
         "# Implementação do Encoder e Decoder Determinístico de Ação\n",
         "\n",
@@ -29,40 +18,37 @@
         "- `pos`: [x, y] coordenadas da ação\n",
         "\n",
         "Note que o encoder não codifica o campo `time`, assume-se que a ação e o frame estarão casados temporalmente na etapa do WFM"
-      ],
-      "metadata": {
-        "id": "hxYwMDg5G7Oz"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "## 1. Dependências"
-      ],
       "metadata": {
         "id": "ZIdMiQqrG_H6"
-      }
+      },
+      "source": [
+        "## 1. Dependências"
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 58,
+      "execution_count": 18,
       "metadata": {
-        "id": "Sdx_icDB2Vdb",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "Sdx_icDB2Vdb",
         "outputId": "4f90b044-33e0-4700-94ee-dcf22aecf545"
       },
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "<torch._C.Generator at 0x79339d090d30>"
+              "<torch._C.Generator at 0x29ae4c99370>"
             ]
           },
+          "execution_count": 18,
           "metadata": {},
-          "execution_count": 58
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -82,17 +68,22 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "cdpGn7BkHDxr"
+      },
       "source": [
         "## 2. Definições do Cenário e Codificação\n",
         "\n",
         "A primeira etapa consiste em codificar uma ação em um tensor do Pytorch. A ausência de ação corresponde à um tensor nulo."
-      ],
-      "metadata": {
-        "id": "cdpGn7BkHDxr"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "8GiQ-3UBIFqz"
+      },
+      "outputs": [],
       "source": [
         "# Dimensões da tela do cenário 1\n",
         "SCREEN_WIDTH = 800\n",
@@ -113,15 +104,15 @@
         "INPUT_VECTOR_DIM = 4\n",
         "\n",
         "LATENT_ACTION_DIM = 16 # Za: Dimensão do Espaço Latente da Ação"
-      ],
-      "metadata": {
-        "id": "8GiQ-3UBIFqz"
-      },
-      "execution_count": 59,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "WyiQULmkJKEz"
+      },
+      "outputs": [],
       "source": [
         "# Função de codificação\n",
         "def encoding_function(action_data: dict = None) -> torch.Tensor:\n",
@@ -159,15 +150,28 @@
         "    raise ValueError(f\"Dimensão do vetor inválida: {len(input_list)}\")\n",
         "\n",
         "  return torch.tensor(input_list, dtype=torch.float32)"
-      ],
-      "metadata": {
-        "id": "WyiQULmkJKEz"
-      },
-      "execution_count": 60,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bTmDJytpTMzP",
+        "outputId": "6c4597da-8721-48d2-aa90-5afda4ff5b02"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Ação presente (mouse_down): [1.0, 1.0, 0.5, 0.5]\n",
+            "Ausência de ação: [0.0, 0.0, 0.0, 0.0]\n"
+          ]
+        }
+      ],
       "source": [
         "# Exemplo de ação mouse_down\n",
         "exemplo_acao = {\n",
@@ -184,39 +188,26 @@
         "# Testando codificação de ausência de ação (No-Op)\n",
         "vetor_none = encoding_function(None)\n",
         "print(\"Ausência de ação:\", vetor_none.tolist())\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bTmDJytpTMzP",
-        "outputId": "6c4597da-8721-48d2-aa90-5afda4ff5b02"
-      },
-      "execution_count": 61,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Ação presente (mouse_down): [1.0, 1.0, 0.5, 0.5]\n",
-            "Ausência de ação: [0.0, 0.0, 0.0, 0.0]\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "dUEH1ACJV0Rz"
+      },
       "source": [
         "## 3. Criando o Encoder e Decoder\n",
         "\n",
         "Agora vamos criar uma rede neural simples que transforma esses 4 features em um embedding mais rico. Depois, vamos fazer o mesmo só que para o movimento contrário. Optou-se por uma rede simples inicialmente, para fins de teste."
-      ],
-      "metadata": {
-        "id": "dUEH1ACJV0Rz"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "skhXNuOlP1uw"
+      },
+      "outputs": [],
       "source": [
         "class TextEncoder(nn.Module):\n",
         "  \"\"\"\n",
@@ -237,15 +228,15 @@
         "\n",
         "  def forward(self, x):\n",
         "    return self.net(x)"
-      ],
-      "metadata": {
-        "id": "skhXNuOlP1uw"
-      },
-      "execution_count": 62,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "tio4iMccYTiB"
+      },
+      "outputs": [],
       "source": [
         "class TextDecoder(nn.Module):\n",
         "  \"\"\"\n",
@@ -286,26 +277,26 @@
         "\n",
         "  def forward(self, z):\n",
         "    return self.net(z)\n"
-      ],
-      "metadata": {
-        "id": "tio4iMccYTiB"
-      },
-      "execution_count": 63,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "qBlYqqB9cBYA"
+      },
       "source": [
         "## 4. Geração de um dataset sintético simples\n",
         "\n",
         "Como a estrutura das ações é muito simples, é mais vantajoso montar um dataset sintético com mais ações do que usar o dataset real com as 200 simulações. Note que na simulação real, menos de 1% dos frames terão uma ação correspondente. Mesmo assim, opta-se por fazer um split de 50/50 em dados com ação positiva e nula. Isso se deve principalmente porque caso a divisão que espelha o comportamento real fosse usada, o modelo não teria incentivos para minimizar a perda das ações com clique, e focaria apenas em minimizar ao máximo o erro da ausência de ação, ignorando o outro caso."
-      ],
-      "metadata": {
-        "id": "qBlYqqB9cBYA"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "ORoCvrBVceZp"
+      },
+      "outputs": [],
       "source": [
         "NUM_SAMPLES = 2000\n",
         "BATCH_SIZE = 64\n",
@@ -333,25 +324,11 @@
         "for _ in range(1000): # 50% de 2000\n",
         "  # Quando não tem ação, passa um None à função de encoding\n",
         "  synthetic_inputs.append(encoding_function(None))"
-      ],
-      "metadata": {
-        "id": "ORoCvrBVceZp"
-      },
-      "execution_count": 64,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "full_tensor = torch.stack(synthetic_inputs)\n",
-        "\n",
-        "# Note que como queremos reconstruir a entrada, ela é a mesma coisa que a saída\n",
-        "dataset = TensorDataset(full_tensor, full_tensor)\n",
-        "dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
-        "\n",
-        "print(f\"Dataset Sintético Gerado: {len(full_tensor)} amostras.\")\n",
-        "print(f\"Shape de um Batch: {next(iter(dataloader))[0].shape}\")"
-      ],
+      "execution_count": 26,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -359,29 +336,45 @@
         "id": "7r3PyF72R1LK",
         "outputId": "cc42ad78-3769-48d7-ca5f-58babd794bf9"
       },
-      "execution_count": 65,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Dataset Sintético Gerado: 2000 amostras.\n",
             "Shape de um Batch: torch.Size([64, 4])\n"
           ]
         }
+      ],
+      "source": [
+        "full_tensor = torch.stack(synthetic_inputs)\n",
+        "\n",
+        "# Note que como queremos reconstruir a entrada, ela é a mesma coisa que a saída\n",
+        "dataset = TensorDataset(full_tensor, full_tensor)\n",
+        "dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
+        "\n",
+        "print(f\"Dataset Sintético Gerado: {len(full_tensor)} amostras.\")\n",
+        "print(f\"Shape de um Batch: {next(iter(dataloader))[0].shape}\")"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "## 5. Treinamento"
-      ],
       "metadata": {
         "id": "U1CNwmw9eGeo"
-      }
+      },
+      "source": [
+        "## 5. Treinamento e validação\n",
+        "\n",
+        "O treinamento e validação são realizados nas células abaixo"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "j2N-9JrVTVBm"
+      },
+      "outputs": [],
       "source": [
         "# Iniciação dos modelos e otimizador\n",
         "text_encoder = TextEncoder()\n",
@@ -393,22 +386,77 @@
         "\n",
         "# Função de Perda é a MSE\n",
         "loss_function = nn.MSELoss()"
-      ],
-      "metadata": {
-        "id": "j2N-9JrVTVBm"
-      },
-      "execution_count": 66,
-      "outputs": []
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Construindo a base de dados para validação\n",
+        "\n",
+        "Para a validação, definiu-se que 20% do dataset estaria destinado para a validação dos resultados, enquanto que os restantes 80% estariam dedicados para o treinamento do modelo."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "### Separando dados para validação\n",
+        "from torch.utils.data import random_split,DataLoader\n",
+        "\n",
+        "##Definindo-se os tamanhos de cada dataset\n",
+        "VAL_RATIO = 0.2 # 20% é destinado para validação\n",
+        "val_size = int(len(dataset) * VAL_RATIO) #Calculado o tamanho do dataset de validação\n",
+        "train_size = len(dataset) -val_size #Calculado o tamanho de dataset de treinamento\n",
+        "\n",
+        "train_dataset, val_dataset = random_split(dataset,[train_size,val_size])  #random_split é executado no dataset a fim de criar cada um dos datasets\n",
+        "#para a respectivo propósito através do random_split\n",
+        "\n",
+        "train_dataloader = DataLoader(train_dataset,batch_size=32,shuffle=True) #Train_dataloader é incializado chamando-se o DataLoader do pytorch e passando o dataset adequado\n",
+        "val_dataloader = DataLoader(val_dataset,batch_size=32,shuffle=False)\n",
+        "\n"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BCrX5bDwUOau",
+        "outputId": "f3af6d83-f596-458b-971a-a42a7bbfc762"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Iniciando treinamento\n",
+            "Epoch [5/20], Loss de Reconstrução: 0.008366\n",
+            "Epoch [5/20] | Train Loss: 0.008366 | Val Loss: 0.012960\n",
+            "Epoch [10/20], Loss de Reconstrução: 0.006950\n",
+            "Epoch [10/20] | Train Loss: 0.006950 | Val Loss: 0.011041\n",
+            "Epoch [15/20], Loss de Reconstrução: 0.005209\n",
+            "Epoch [15/20] | Train Loss: 0.005209 | Val Loss: 0.007972\n",
+            "Epoch [20/20], Loss de Reconstrução: 0.003146\n",
+            "Epoch [20/20] | Train Loss: 0.003146 | Val Loss: 0.004664\n"
+          ]
+        }
+      ],
       "source": [
         "NUM_EPOCHS = 20\n",
         "print(\"Iniciando treinamento\")\n",
         "\n",
         "train_losses = [] # Perdas de treinamento para plotar depois\n",
+        "val_losses = [] # Perdas de validação que serão usadas para plotar depois os resultados\n",
         "\n",
         "for epoch in range(NUM_EPOCHS):\n",
+        "    #Treinamento\n",
+        "    text_decoder.train()\n",
+        "    text_decoder.train()\n",
         "    total_loss = 0  # Acumula a perda total de cada época\n",
         "\n",
         "    # Loop dos batches\n",
@@ -429,34 +477,207 @@
         "\n",
         "        total_loss += loss.item()\n",
         "\n",
-        "    avg_loss = total_loss / len(dataloader)\n",
-        "    train_losses.append(avg_loss)\n",
+        "    avg_train_loss = total_loss/ len(train_dataloader)\n",
+        "    #avg_loss = total_loss / len(dataloader)\n",
+        "    train_losses.append(avg_train_loss)\n",
+        "\n",
+        "    #Iniciada a validação do treinamento\n",
+        "    #Validação\n",
+        "    text_encoder.eval()\n",
+        "    text_decoder.eval()\n",
+        "    val_loss_total = 0\n",
+        "\n",
+        "    with torch.no_grad():\n",
+        "        for data,_ in val_dataloader:\n",
+        "            z_a = text_encoder(data)\n",
+        "            reconstruction = text_decoder(z_a)\n",
+        "            val_loss = loss_function(reconstruction,data)\n",
+        "            val_loss_total += val_loss.item()\n",
+        "\n",
+        "    avg_val_loss = val_loss_total / len(val_dataloader)\n",
+        "    val_losses.append(avg_val_loss)\n",
         "\n",
         "    # Mostra a perda a cada 5 épocas\n",
         "    if (epoch + 1) % 5 == 0:\n",
-        "        print(f\"Epoch [{epoch+1}/{NUM_EPOCHS}], Loss de Reconstrução: {avg_loss:.6f}\")"
+        "        print(f\"Epoch [{epoch+1}/{NUM_EPOCHS}], Loss de Reconstrução: {avg_train_loss:.6f}\")\n",
+        "        print(f\"Epoch [{epoch+1}/{NUM_EPOCHS}] | Train Loss: {avg_train_loss:.6f} | Val Loss: {avg_val_loss:.6f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plotagem dos resultados de treinamento e validação\n",
+        "Uma vez que o modelo tenha sido devidamente treinado e validado, faz-se importante realizar a representação visual dos dados ao longo do tempo, de tal maneira a representar assim as percas obtidas ao longo do tempo.\n",
+        "A imagem abaixo representa as perdas totais de validação e treinamento."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "BCrX5bDwUOau",
-        "outputId": "f3af6d83-f596-458b-971a-a42a7bbfc762"
-      },
-      "execution_count": 67,
+      "source": [
+        "plt.figure(figsize=(8,5))\n",
+        "plt.plot(train_losses,label=\"Treinamento\",marker='o')\n",
+        "plt.plot(val_losses,label=\"Validação\",marker='s')\n",
+        "plt.title(\"Evolução da perda de reconstrução\")\n",
+        "plt.xlabel(\"época\")\n",
+        "plt.ylabel(\"Loss (MSE)\")\n",
+        "plt.grid(True,linestyle='--',alpha=0.6)\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "É possível observar através da imagem que o gráfico está caindo constantemente e que não apresenta indícios de overfitting"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Feedbacks adicionais quanto ao treinamento"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "best_epoch = int(np.argmin(val_losses))\n",
+        "plt.figure(figsize=(8,5))\n",
+        "plt.plot(train_losses, label=\"Treino\", marker='o')\n",
+        "plt.plot(val_losses, label=\"Validação\", marker='s')\n",
+        "plt.axvline(best_epoch, linestyle='--', alpha=0.6, label=f\"Best @ {best_epoch}\")\n",
+        "plt.title(\"Perda de reconstrução vs época\")\n",
+        "plt.xlabel(\"Época\"); plt.ylabel(\"Loss (MSE)\"); plt.grid(True, ls='--', alpha=0.5); plt.legend(); plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Exportação de métricas por época (CSV)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {},
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
+          "text": [
+            "Valores salvos em training_history.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "history = pd.DataFrame({\"epoch\":list(range(1,len(train_losses)+1)),\n",
+        "                        \"train_loss\": train_losses, \"val_loss\":val_losses})\n",
+        "history.to_csv(\"training_history.csv\",index=False)\n",
+        "print(\"Valores salvos em training_history.csv\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Erro por dimensão (quais campos erram mais?)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "MAE => Mean Absolute Error (erro médio de reconstrução em cada campo do vetor de entrada)\n",
+        "\n",
+        "[type, object, x , y]\n",
+        "tipo de ação,\n",
+        "object => objeto afetado,\n",
+        "x => Posição x,\n",
+        "y => Posição y;\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {},
+      "outputs": [
+        {
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "Iniciando treinamento\n",
-            "Epoch [5/20], Loss de Reconstrução: 0.013939\n",
-            "Epoch [10/20], Loss de Reconstrução: 0.010992\n",
-            "Epoch [15/20], Loss de Reconstrução: 0.008542\n",
-            "Epoch [20/20], Loss de Reconstrução: 0.003926\n"
+            "MAE por dimensão: [0.02663636 0.04098256 0.05402165 0.0424518 ]\n"
           ]
         }
+      ],
+      "source": [
+        "import torch\n",
+        "text_encoder.eval(); text_decoder.eval()\n",
+        "per_dim_mae = torch.zeros(data.shape[1])\n",
+        "n = 0\n",
+        "with torch.no_grad():\n",
+        "    for data, _ in val_dataloader:\n",
+        "        rec = text_decoder(text_encoder(data))\n",
+        "        per_dim_mae += (rec - data).abs().mean(dim=0)\n",
+        "        n += 1\n",
+        "per_dim_mae /= n\n",
+        "print(\"MAE por dimensão:\", per_dim_mae.detach().cpu().numpy())\n"
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "torch251",
+      "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": "3.12.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From b4e2cb783d161550d96a55721adc9736c8610d7f Mon Sep 17 00:00:00 2001
From: felipe gibin <gibinf.duarte@gmail.com>
Date: Mon, 3 Nov 2025 21:08:03 -0300
Subject: [PATCH 4/4] =?UTF-8?q?fix:=20remove=20duplicata=20modelo=20decode?=
 =?UTF-8?q?r=20feat:=20adiciona=20avalia=C3=A7=C3=A3o=20num=C3=A9rica=20si?=
 =?UTF-8?q?mples?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 models/Text_Encoder.ipynb | 268 +++++++++++++++++++++++++++-----------
 1 file changed, 190 insertions(+), 78 deletions(-)

diff --git a/models/Text_Encoder.ipynb b/models/Text_Encoder.ipynb
index 1d95a5f..5621c49 100644
--- a/models/Text_Encoder.ipynb
+++ b/models/Text_Encoder.ipynb
@@ -31,24 +31,24 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": 26,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "Sdx_icDB2Vdb",
-        "outputId": "4f90b044-33e0-4700-94ee-dcf22aecf545"
+        "outputId": "3d0728ea-c642-4a6b-e818-0e84a42c927c"
       },
       "outputs": [
         {
+          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "<torch._C.Generator at 0x29ae4c99370>"
+              "<torch._C.Generator at 0x7af908048d70>"
             ]
           },
-          "execution_count": 18,
           "metadata": {},
-          "output_type": "execute_result"
+          "execution_count": 26
         }
       ],
       "source": [
@@ -79,7 +79,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": 27,
       "metadata": {
         "id": "8GiQ-3UBIFqz"
       },
@@ -108,7 +108,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "execution_count": 28,
       "metadata": {
         "id": "WyiQULmkJKEz"
       },
@@ -154,18 +154,18 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": 29,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "bTmDJytpTMzP",
-        "outputId": "6c4597da-8721-48d2-aa90-5afda4ff5b02"
+        "outputId": "753ab7be-8f89-473b-853e-c495e4385d31"
       },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Ação presente (mouse_down): [1.0, 1.0, 0.5, 0.5]\n",
             "Ausência de ação: [0.0, 0.0, 0.0, 0.0]\n"
@@ -203,7 +203,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": 30,
       "metadata": {
         "id": "skhXNuOlP1uw"
       },
@@ -232,7 +232,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": 31,
       "metadata": {
         "id": "tio4iMccYTiB"
       },
@@ -255,28 +255,7 @@
         "\n",
         "  def forward(self, z):\n",
         "    x = self.net(z)\n",
-        "    return torch.sigmoid(x)\n",
-        "\n",
-        "\n",
-        "class TextDecoder(nn.Module):\n",
-        "  \"\"\"\n",
-        "  Decoder determinístico (reconstrução)\n",
-        "  Mapeia o vetor latente de volta para o espaço da ação original\n",
-        "  \"\"\"\n",
-        "\n",
-        "  def __init__(self, latent_dim=LATENT_ACTION_DIM, output_dim=INPUT_VECTOR_DIM):\n",
-        "    super().__init__()\n",
-        "\n",
-        "    self.net = nn.Sequential(\n",
-        "      nn.Linear(latent_dim, 32),\n",
-        "      nn.ReLU(),\n",
-        "      nn.Linear(32, 16),\n",
-        "      nn.ReLU(),\n",
-        "      nn.Linear(16, output_dim)\n",
-        "    )\n",
-        "\n",
-        "  def forward(self, z):\n",
-        "    return self.net(z)\n"
+        "    return torch.sigmoid(x)"
       ]
     },
     {
@@ -292,7 +271,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": 32,
       "metadata": {
         "id": "ORoCvrBVceZp"
       },
@@ -328,18 +307,18 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": 33,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "7r3PyF72R1LK",
-        "outputId": "cc42ad78-3769-48d7-ca5f-58babd794bf9"
+        "outputId": "6f38085c-7097-4cb7-c834-2789c8dd27fd"
       },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Dataset Sintético Gerado: 2000 amostras.\n",
             "Shape de um Batch: torch.Size([64, 4])\n"
@@ -370,7 +349,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
+      "execution_count": 34,
       "metadata": {
         "id": "j2N-9JrVTVBm"
       },
@@ -390,7 +369,9 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "5M8q7ijBJFob"
+      },
       "source": [
         "#### Construindo a base de dados para validação\n",
         "\n",
@@ -399,8 +380,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
+      "execution_count": 35,
+      "metadata": {
+        "id": "1kiV2KC3JFoc"
+      },
       "outputs": [],
       "source": [
         "### Separando dados para validação\n",
@@ -421,28 +404,28 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 36,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "BCrX5bDwUOau",
-        "outputId": "f3af6d83-f596-458b-971a-a42a7bbfc762"
+        "outputId": "a3c5b769-1791-4596-b311-ddfb0a3058db"
       },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Iniciando treinamento\n",
-            "Epoch [5/20], Loss de Reconstrução: 0.008366\n",
-            "Epoch [5/20] | Train Loss: 0.008366 | Val Loss: 0.012960\n",
-            "Epoch [10/20], Loss de Reconstrução: 0.006950\n",
-            "Epoch [10/20] | Train Loss: 0.006950 | Val Loss: 0.011041\n",
-            "Epoch [15/20], Loss de Reconstrução: 0.005209\n",
-            "Epoch [15/20] | Train Loss: 0.005209 | Val Loss: 0.007972\n",
-            "Epoch [20/20], Loss de Reconstrução: 0.003146\n",
-            "Epoch [20/20] | Train Loss: 0.003146 | Val Loss: 0.004664\n"
+            "Epoch [5/20], Loss de Reconstrução: 0.014194\n",
+            "Epoch [5/20] | Train Loss: 0.014194 | Val Loss: 0.015762\n",
+            "Epoch [10/20], Loss de Reconstrução: 0.003613\n",
+            "Epoch [10/20] | Train Loss: 0.003613 | Val Loss: 0.005375\n",
+            "Epoch [15/20], Loss de Reconstrução: 0.000926\n",
+            "Epoch [15/20] | Train Loss: 0.000926 | Val Loss: 0.000958\n",
+            "Epoch [20/20], Loss de Reconstrução: 0.000083\n",
+            "Epoch [20/20] | Train Loss: 0.000083 | Val Loss: 0.000128\n"
           ]
         }
       ],
@@ -505,7 +488,9 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "8KFGgY8MJFoe"
+      },
       "source": [
         "## Plotagem dos resultados de treinamento e validação\n",
         "Uma vez que o modelo tenha sido devidamente treinado e validado, faz-se importante realizar a representação visual dos dados ao longo do tempo, de tal maneira a representar assim as percas obtidas ao longo do tempo.\n",
@@ -514,18 +499,25 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 34,
-      "metadata": {},
+      "execution_count": 37,
+      "metadata": {
+        "id": "SP1kpFdKJFoe",
+        "outputId": "30792517-8e7d-4722-872b-d1f0f2524986",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 488
+        }
+      },
       "outputs": [
         {
+          "output_type": "display_data",
           "data": {
-            "image/png": 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",
             "text/plain": [
               "<Figure size 800x500 with 1 Axes>"
-            ]
+            ],
+            "image/png": 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\n"
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         }
       ],
       "source": [
@@ -542,32 +534,43 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "UzlyAaeCJFof"
+      },
       "source": [
         "É possível observar através da imagem que o gráfico está caindo constantemente e que não apresenta indícios de overfitting"
       ]
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "FUY7agyBJFof"
+      },
       "source": [
         "## Feedbacks adicionais quanto ao treinamento"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 35,
-      "metadata": {},
+      "execution_count": 38,
+      "metadata": {
+        "id": "kCXbelSPJFog",
+        "outputId": "4c1fb895-570f-4071-e9e7-2817b1242755",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 490
+        }
+      },
       "outputs": [
         {
+          "output_type": "display_data",
           "data": {
-            "image/png": 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",
             "text/plain": [
               "<Figure size 800x500 with 1 Axes>"
-            ]
+            ],
+            "image/png": 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\n"
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         }
       ],
       "source": [
@@ -582,7 +585,9 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "6C5sQBSjJFoh"
+      },
       "source": [
         "### Exportação de métricas por época (CSV)"
       ]
@@ -590,11 +595,17 @@
     {
       "cell_type": "code",
       "execution_count": 39,
-      "metadata": {},
+      "metadata": {
+        "id": "27Jx-MNwJFoh",
+        "outputId": "525cb3e6-7731-4068-a34e-512d015d422e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Valores salvos em training_history.csv\n"
           ]
@@ -610,14 +621,18 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "BwU9QILdJFoi"
+      },
       "source": [
         "### Erro por dimensão (quais campos erram mais?)"
       ]
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "jaXgbkzXJFoi"
+      },
       "source": [
         "MAE => Mean Absolute Error (erro médio de reconstrução em cada campo do vetor de entrada)\n",
         "\n",
@@ -630,14 +645,20 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 41,
-      "metadata": {},
+      "execution_count": 40,
+      "metadata": {
+        "id": "BvF3AZF8JFoi",
+        "outputId": "849acd56-1418-4440-856f-891884081b18",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
-            "MAE por dimensão: [0.02663636 0.04098256 0.05402165 0.0424518 ]\n"
+            "MAE por dimensão: [0.00555877 0.00672707 0.0130372  0.00728562]\n"
           ]
         }
       ],
@@ -654,6 +675,97 @@
         "per_dim_mae /= n\n",
         "print(\"MAE por dimensão:\", per_dim_mae.detach().cpu().numpy())\n"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Avaliação simples numérica"
+      ],
+      "metadata": {
+        "id": "YJ71zxnuLTpA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Avaliação simples\n",
+        "with torch.no_grad():\n",
+        "    # Teste 1 — Ação Nula (No-Op)\n",
+        "\n",
+        "    # Vetor que representa ausência de ação\n",
+        "    v_none_original = encoding_function(None)\n",
+        "\n",
+        "    # Passa pelo encoder e depois pelo decoder\n",
+        "    z_none = text_encoder(v_none_original.unsqueeze(0))\n",
+        "    reconstruction_none = text_decoder(z_none).squeeze(0)\n",
+        "\n",
+        "    print(\"Teste 1: Reconstrução da ação nula\")\n",
+        "    print(f\"Original:     {v_none_original.tolist()}\")\n",
+        "    print(f\"Reconstruído: {[round(x, 4) for x in reconstruction_none.tolist()]}\")\n",
+        "    print()\n",
+        "\n",
+        "    # Teste 2 — Ação Positiva (Mouse Down)\n",
+        "    pos_x, pos_y = 640, 120\n",
+        "\n",
+        "    test_action = {\n",
+        "        \"type\": \"mouse_down\",\n",
+        "        \"object\": \"ball\",\n",
+        "        \"pos\": [pos_x, pos_y]\n",
+        "    }\n",
+        "\n",
+        "    v_down_original = encoding_function(test_action)\n",
+        "\n",
+        "    # Codifica e decodifica\n",
+        "    z_down = text_encoder(v_down_original.unsqueeze(0))\n",
+        "    reconstruction_down = text_decoder(z_down).squeeze(0)\n",
+        "\n",
+        "    # Coordenadas normalizadas esperadas\n",
+        "    x_expected = pos_x / SCREEN_WIDTH\n",
+        "    y_expected = pos_y / SCREEN_HEIGHT\n",
+        "\n",
+        "    print(\"Teste 2: Reconstrução de ação mouse_down\")\n",
+        "    print(f\"Original:     {v_down_original.tolist()}\")\n",
+        "    print(f\"Reconstruído: {[round(x, 4) for x in reconstruction_down.tolist()]}\")\n",
+        "    print()\n",
+        "\n",
+        "    # Análise das posições normalizadas\n",
+        "    print(\"Componentes de posição (normalizados):\")\n",
+        "    print(f\"Original:     x={x_expected:.4f}, y={y_expected:.4f}\")\n",
+        "    print(f\"Reconstruído: x={reconstruction_down[2]:.4f}, y={reconstruction_down[3]:.4f}\")\n",
+        "\n",
+        "    # Erro máximo absoluto\n",
+        "    max_error = torch.abs(reconstruction_down - v_down_original).max().item()\n",
+        "    print(f\"\\nErro máximo absoluto: {max_error:.6f}\")\n"
+      ],
+      "metadata": {
+        "id": "Gy4obUfoJpoU",
+        "outputId": "0f629391-f1c9-4d2d-db23-bfcadbfb7e86",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Teste 1: Reconstrução da ação nula\n",
+            "Original:     [0.0, 0.0, 0.0, 0.0]\n",
+            "Reconstruído: [0.0067, 0.0086, 0.01, 0.0034]\n",
+            "\n",
+            "Teste 2: Reconstrução de ação mouse_down\n",
+            "Original:     [1.0, 1.0, 0.800000011920929, 0.20000000298023224]\n",
+            "Reconstruído: [0.9996, 0.9998, 0.8038, 0.206]\n",
+            "\n",
+            "Componentes de posição (normalizados):\n",
+            "Original:     x=0.8000, y=0.2000\n",
+            "Reconstruído: x=0.8038, y=0.2060\n",
+            "\n",
+            "Erro máximo absoluto: 0.005991\n"
+          ]
+        }
+      ]
     }
   ],
   "metadata": {
@@ -680,4 +792,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}
\ No newline at end of file