From a3f00b0d6005ee3270c7303041c3e4987ea4d635 Mon Sep 17 00:00:00 2001 From: Alejandro Lembke Barrientos Date: Mon, 10 Aug 2026 00:29:32 +0000 Subject: [PATCH] Adding First Lab. --- Lab1/data/dataset_A_dev.csv | 201 ++++ Lab1/data/dataset_A_train.csv | 1001 ++++++++++++++++++ Lab1/data/dataset_B_dev.csv | 201 ++++ Lab1/data/dataset_B_train.csv | 1001 ++++++++++++++++++ Lab1/data/dataset_C_dev.csv | 201 ++++ Lab1/data/dataset_C_train.csv | 1001 ++++++++++++++++++ Lab1/data/dataset_D_dev.csv | 201 ++++ Lab1/data/dataset_D_train.csv | 1001 ++++++++++++++++++ Lab1/lab1_mlp.ipynb | 1812 +++++++++++++++++++++++++++++++++ Lab1/public_tests.py | 158 +++ README.md | 12 + 11 files changed, 6790 insertions(+) create mode 100755 Lab1/data/dataset_A_dev.csv create mode 100755 Lab1/data/dataset_A_train.csv create mode 100755 Lab1/data/dataset_B_dev.csv create mode 100755 Lab1/data/dataset_B_train.csv create mode 100755 Lab1/data/dataset_C_dev.csv create mode 100755 Lab1/data/dataset_C_train.csv create mode 100755 Lab1/data/dataset_D_dev.csv create mode 100755 Lab1/data/dataset_D_train.csv create mode 100755 Lab1/lab1_mlp.ipynb create mode 100755 Lab1/public_tests.py create mode 100644 README.md diff --git a/Lab1/data/dataset_A_dev.csv b/Lab1/data/dataset_A_dev.csv new file mode 100755 index 0000000..792d283 --- /dev/null +++ b/Lab1/data/dataset_A_dev.csv @@ -0,0 +1,201 @@ +x1,x2,y +1.475419709720659,1.777920234120697,1 +2.043213278812804,-2.244985311465688,0 +1.53402746415258,0.9550498334596311,1 +1.221313635232566,-1.231394344278059,0 +1.2660591028374135,1.2141337205636444,1 +1.308281729663989,1.6243709848784922,1 +1.1666152470476476,1.2285325028094338,1 +1.3900602715631172,0.4061032304095369,1 +2.877815055120691,-3.2190466283388375,0 +1.6012436653445823,0.8655384201156864,1 +2.6359356979234825,-2.947983748632429,0 +1.9225526795448395,1.623790790476797,1 +1.2290413977963737,-1.215622204524599,0 +2.893268492074382,-3.2329910395211745,0 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+18.006838705865835,31.977585511317788,1,1,1,1 +17.02506041635571,37.005905173250326,1,0,1,0 +22.96858400073058,33.98608167798495,1,0,1,1 +19.04675543805114,32.04556140025953,1,0,1,1 +22.003922506538895,23.996916734039672,1,1,0,0 +22.984753891453042,27.96548352104111,1,0,0,0 +17.02802641376364,24.968357827257393,1,0,0,1 diff --git a/Lab1/lab1_mlp.ipynb b/Lab1/lab1_mlp.ipynb new file mode 100755 index 0000000..5230be4 --- /dev/null +++ b/Lab1/lab1_mlp.ipynb @@ -0,0 +1,1812 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "VVluUyWzCFLy", + "metadata": { + "id": "VVluUyWzCFLy" + }, + "source": [ + "# Laboratorio: MLPs, verosimilitud, entrenamiento y evaluación\n", + "\n", + "**Nombre:** Michaelle Perez \n", + "**Institución:** BioMedLab Galileo - deeplearning.ai \n", + "\n", + "Este laboratorio acompaña la sesión sobre perceptrones multicapa. El objetivo es practicar el flujo completo:\n", + "\n", + "1. creación y carga de datasets;\n", + "2. visualización;\n", + "3. normalización y estandarización;\n", + "4. construcción de redes MLP;\n", + "5. entrenamiento;\n", + "6. evaluación con métricas apropiadas;\n", + "7. pruebas públicas y privadas.\n", + "\n", + "Las notas de clase proveen el desarrollo teórico completo. Este notebook se enfoca en ejemplos computacionales y tareas implementables.\n", + "\n", + "> Importante: en las celdas Markdown se usa `$$...$$` para bloques de LaTeX.\n" + ] + }, + { + "cell_type": "markdown", + "id": "F3Ru2r0xCFLz", + "metadata": { + "id": "F3Ru2r0xCFLz" + }, + "source": [ + "## Identificación del estudiante\n", + "\n", + "Antes de ejecutar pruebas privadas, cambie los valores de `ID` y `NOMBRE`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "-S0jst8YCFLz", + "metadata": { + "id": "-S0jst8YCFLz" + }, + "outputs": [], + "source": [ + "ID = \"12002840\"\n", + "NOMBRE = \"Alejandro Lembke Barrientos\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "vXNfE06SCFLz", + "metadata": { + "id": "vXNfE06SCFLz" + }, + "source": [ + "## Imports\n", + "\n", + "Este laboratorio usa `numpy`, `pandas`, `matplotlib`, `scikit-learn` y `torch`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ki35HkfFCFLz", + "metadata": { + "id": "ki35HkfFCFLz" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PyTorch: 2.10.0a0+b4e4ee81d3.nv25.12\n", + "Data directory: /workspace/data\n" + ] + } + ], + "source": [ + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.datasets import make_classification, make_multilabel_classification\n", + "from sklearn.model_selection import train_test_split, KFold\n", + "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", + "from sklearn.metrics import (\n", + " accuracy_score,\n", + " precision_score,\n", + " recall_score,\n", + " f1_score,\n", + " confusion_matrix,\n", + " mean_squared_error,\n", + " mean_absolute_error,\n", + " r2_score,\n", + ")\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from torch.utils.data import TensorDataset, DataLoader\n", + "\n", + "torch.manual_seed(42)\n", + "np.random.seed(42)\n", + "\n", + "DATA_DIR = Path(\"data\")\n", + "DATA_DIR.mkdir(exist_ok=True)\n", + "\n", + "print(\"PyTorch:\", torch.__version__)\n", + "print(\"Data directory:\", DATA_DIR.resolve())\n" + ] + }, + { + "cell_type": "markdown", + "id": "33ee73f5", + "metadata": {}, + "source": [ + "## Disponibilidad de `public_tests` en el kernel\n", + "\n", + "Este notebook se ejecuta contra un kernel de Jupyter que corre en **otra máquina**, así que el kernel no ve los archivos locales del laboratorio. Solo trabaja con su propio directorio de trabajo.\n", + "\n", + "Eso no afecta a `data/`: la sección 1 regenera los cuatro datasets con semillas fijas, de modo que los CSV se crean en el directorio de trabajo del kernel.\n", + "\n", + "Sí afecta a `public_tests.py`, que no existe en el kernel remoto. La siguiente celda usa la magia `%%writefile` para materializarlo ahí con el mismo contenido que entregó el instructor, y la celda posterior lo importa. Si el notebook se corriera en local, simplemente reescribe el archivo con contenido idéntico.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f0a930c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing public_tests.py\n" + ] + } + ], + "source": [ + "%%writefile public_tests.py\n", + "\"\"\"\n", + "Public tests for the MLP lab.\n", + "\n", + "Usage inside the notebook:\n", + " import public_tests as public_tests\n", + " public_tests.test_tarea1(globals())\n", + " public_tests.test_tarea2(globals())\n", + " public_tests.test_tarea3(globals())\n", + "\n", + "These tests are intentionally lightweight. Passing public tests does not guarantee\n", + "full credit in the private grader.\n", + "\"\"\"\n", + "\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import pandas as pd\n", + "import torch\n", + "import torch.nn as nn\n", + "from torch.utils.data import TensorDataset, DataLoader\n", + "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", + "\n", + "DATA_DIR = Path(\"data\")\n", + "\n", + "\n", + "def _require(namespace, name):\n", + " assert name in namespace, f\"Missing required object: {name}\"\n", + " return namespace[name]\n", + "\n", + "\n", + "def _preprocess_features(train_df, dev_df, feature_cols=(\"x1\", \"x2\")):\n", + " X_train = train_df[list(feature_cols)].to_numpy(dtype=np.float32)\n", + " X_dev = dev_df[list(feature_cols)].to_numpy(dtype=np.float32)\n", + " all_X = np.vstack([X_train, X_dev])\n", + " normalizer = MinMaxScaler()\n", + " normalizer.fit(all_X)\n", + " X_train_norm = normalizer.transform(X_train)\n", + " X_dev_norm = normalizer.transform(X_dev)\n", + " standardizer = StandardScaler()\n", + " standardizer.fit(X_train_norm)\n", + " X_train_std = standardizer.transform(X_train_norm).astype(np.float32)\n", + " X_dev_std = standardizer.transform(X_dev_norm).astype(np.float32)\n", + " return X_train_std, X_dev_std\n", + "\n", + "\n", + "def _loader_regression(batch_size=64):\n", + " train_df = pd.read_csv(DATA_DIR / \"dataset_B_train.csv\")\n", + " dev_df = pd.read_csv(DATA_DIR / \"dataset_B_dev.csv\")\n", + " X_train, X_dev = _preprocess_features(train_df, dev_df)\n", + " y_train = train_df[\"y\"].to_numpy(dtype=np.float32).reshape(-1, 1)\n", + " y_dev = dev_df[\"y\"].to_numpy(dtype=np.float32).reshape(-1, 1)\n", + " train_ds = TensorDataset(torch.tensor(X_train), torch.tensor(y_train))\n", + " dev_ds = TensorDataset(torch.tensor(X_dev), torch.tensor(y_dev))\n", + " return DataLoader(train_ds, batch_size=batch_size, shuffle=True), DataLoader(dev_ds, batch_size=256)\n", + "\n", + "\n", + "def _loader_multiclass(batch_size=64):\n", + " train_df = pd.read_csv(DATA_DIR / \"dataset_C_train.csv\")\n", + " dev_df = pd.read_csv(DATA_DIR / \"dataset_C_dev.csv\")\n", + " X_train, X_dev = _preprocess_features(train_df, dev_df)\n", + " y_train = train_df[\"y\"].to_numpy(dtype=np.int64)\n", + " y_dev = dev_df[\"y\"].to_numpy(dtype=np.int64)\n", + " train_ds = TensorDataset(torch.tensor(X_train), torch.tensor(y_train))\n", + " dev_ds = TensorDataset(torch.tensor(X_dev), torch.tensor(y_dev))\n", + " return DataLoader(train_ds, batch_size=batch_size, shuffle=True), DataLoader(dev_ds, batch_size=256)\n", + "\n", + "\n", + "def _loader_multilabel(batch_size=64):\n", + " train_df = pd.read_csv(DATA_DIR / \"dataset_D_train.csv\")\n", + " dev_df = pd.read_csv(DATA_DIR / \"dataset_D_dev.csv\")\n", + " X_train, X_dev = _preprocess_features(train_df, dev_df)\n", + " y_cols = [\"y0\", \"y1\", \"y2\", \"y3\"]\n", + " y_train = train_df[y_cols].to_numpy(dtype=np.float32)\n", + " y_dev = dev_df[y_cols].to_numpy(dtype=np.float32)\n", + " train_ds = TensorDataset(torch.tensor(X_train), torch.tensor(y_train))\n", + " dev_ds = TensorDataset(torch.tensor(X_dev), torch.tensor(y_dev))\n", + " return DataLoader(train_ds, batch_size=batch_size, shuffle=True), DataLoader(dev_ds, batch_size=256)\n", + "\n", + "\n", + "def _count_linears(model):\n", + " return [m for m in model.modules() if isinstance(m, nn.Linear)]\n", + "\n", + "\n", + "def _has_relu(model):\n", + " return any(isinstance(m, nn.ReLU) for m in model.modules())\n", + "\n", + "\n", + "def _assert_three_layer_mlp(model, output_dim):\n", + " linears = _count_linears(model)\n", + " assert len(linears) == 3, \"The model must contain exactly 3 Linear layers.\"\n", + " assert linears[0].in_features == 2, \"The first Linear layer must receive 2 input features.\"\n", + " assert linears[0].out_features == 8, \"The first hidden layer must have 8 units.\"\n", + " assert linears[1].in_features == 8 and linears[1].out_features == 8, \"The second hidden layer must be 8 -> 8.\"\n", + " assert linears[2].in_features == 8 and linears[2].out_features == output_dim, f\"The output layer must have {output_dim} units.\"\n", + " assert _has_relu(model), \"The model should include ReLU activations.\"\n", + "\n", + "\n", + "def test_tarea1(namespace):\n", + " torch.manual_seed(123)\n", + " RegressionMLP = _require(namespace, \"RegressionMLP\")\n", + " train_fn = _require(namespace, \"train_regression_model\")\n", + " eval_fn = _require(namespace, \"evaluate_regression_model\")\n", + " model = RegressionMLP()\n", + " assert isinstance(model, nn.Module), \"RegressionMLP must be a torch.nn.Module.\"\n", + " _assert_three_layer_mlp(model, output_dim=1)\n", + " x = torch.randn(5, 2)\n", + " out = model(x)\n", + " assert tuple(out.shape) == (5, 1), \"RegressionMLP forward output must have shape (batch, 1).\"\n", + " train_loader, dev_loader = _loader_regression()\n", + " _ = train_fn(model, train_loader, epochs=5, lr=1e-2)\n", + " metrics = eval_fn(model, dev_loader)\n", + " assert isinstance(metrics, dict), \"evaluate_regression_model must return a dictionary.\"\n", + " for key in [\"mse\", \"rmse\", \"mae\", \"r2\"]:\n", + " assert key in metrics, f\"Missing regression metric: {key}\"\n", + " assert np.isfinite(metrics[key]), f\"Metric {key} must be finite.\"\n", + " print(\"Tarea 1 public tests passed.\")\n", + "\n", + "\n", + "def test_tarea2(namespace):\n", + " torch.manual_seed(123)\n", + " MulticlassMLP = _require(namespace, \"MulticlassMLP\")\n", + " train_fn = _require(namespace, \"train_multiclass_model\")\n", + " eval_fn = _require(namespace, \"evaluate_multiclass_model\")\n", + " model = MulticlassMLP()\n", + " assert isinstance(model, nn.Module), \"MulticlassMLP must be a torch.nn.Module.\"\n", + " _assert_three_layer_mlp(model, output_dim=4)\n", + " x = torch.randn(5, 2)\n", + " out = model(x)\n", + " assert tuple(out.shape) == (5, 4), \"MulticlassMLP forward output must have shape (batch, 4).\"\n", + " train_loader, dev_loader = _loader_multiclass()\n", + " _ = train_fn(model, train_loader, epochs=5, lr=1e-2)\n", + " metrics = eval_fn(model, dev_loader)\n", + " assert isinstance(metrics, dict), \"evaluate_multiclass_model must return a dictionary.\"\n", + " for key in [\"accuracy\", \"macro_f1\", \"confusion_matrix\"]:\n", + " assert key in metrics, f\"Missing multiclass metric: {key}\"\n", + " assert np.isfinite(metrics[\"accuracy\"]), \"accuracy must be finite.\"\n", + " assert np.isfinite(metrics[\"macro_f1\"]), \"macro_f1 must be finite.\"\n", + " print(\"Tarea 2 public tests passed.\")\n", + "\n", + "\n", + "def test_tarea3(namespace):\n", + " torch.manual_seed(123)\n", + " MultilabelMLP = _require(namespace, \"MultilabelMLP\")\n", + " train_fn = _require(namespace, \"train_multilabel_model\")\n", + " eval_fn = _require(namespace, \"evaluate_multilabel_model\")\n", + " model = MultilabelMLP()\n", + " assert isinstance(model, nn.Module), \"MultilabelMLP must be a torch.nn.Module.\"\n", + " _assert_three_layer_mlp(model, output_dim=4)\n", + " x = torch.randn(5, 2)\n", + " out = model(x)\n", + " assert tuple(out.shape) == (5, 4), \"MultilabelMLP forward output must have shape (batch, 4).\"\n", + " train_loader, dev_loader = _loader_multilabel()\n", + " _ = train_fn(model, train_loader, epochs=5, lr=1e-2)\n", + " metrics = eval_fn(model, dev_loader)\n", + " assert isinstance(metrics, dict), \"evaluate_multilabel_model must return a dictionary.\"\n", + " for key in [\"subset_accuracy\", \"micro_f1\", \"macro_f1\"]:\n", + " assert key in metrics, f\"Missing multilabel metric: {key}\"\n", + " assert np.isfinite(metrics[key]), f\"Metric {key} must be finite.\"\n", + " print(\"Tarea 3 public tests passed.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "33d0908e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "public_tests listo en: /workspace/public_tests.py\n" + ] + } + ], + "source": [ + "import importlib\n", + "\n", + "import public_tests\n", + "importlib.reload(public_tests)\n", + "\n", + "print(\"public_tests listo en:\", Path(\"public_tests.py\").resolve())\n" + ] + }, + { + "cell_type": "markdown", + "id": "EX1hFdMuCFL0", + "metadata": { + "id": "EX1hFdMuCFL0" + }, + "source": [ + "# 1. Creación de datasets\n", + "\n", + "Se crearán cuatro datasets con 1,000 ejemplos para entrenamiento y 200 para desarrollo.\n", + "\n", + "| Dataset | Tipo | Entradas | Salida |\n", + "|---|---|---:|---|\n", + "| A | Clasificación binaria | 2 features | 1 etiqueta binaria |\n", + "| B | Regresión no lineal de orden 5 | 2 features | 1 valor continuo |\n", + "| C | Clasificación multiclase | 2 features | 4 clases |\n", + "| D | Clasificación multilabel | 2 features | 4 etiquetas binarias |\n", + "\n", + "Los datasets se guardan como CSV para luego cargarlos desde archivo, simulando un flujo real de trabajo.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "WLJPSxh2CFL0", + "metadata": { + "id": "WLJPSxh2CFL0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data/dataset_A_dev.csv (200, 3)\n", + "data/dataset_A_train.csv (1000, 3)\n", + "data/dataset_B_dev.csv (200, 3)\n", + "data/dataset_B_train.csv (1000, 3)\n", + "data/dataset_C_dev.csv (200, 3)\n", + "data/dataset_C_train.csv (1000, 3)\n", + "data/dataset_D_dev.csv (200, 6)\n", + "data/dataset_D_train.csv (1000, 6)\n" + ] + } + ], + "source": [ + "rng = np.random.default_rng(42)\n", + "\n", + "# Dataset A: clasificación binaria, 2 features\n", + "XA, yA = make_classification(\n", + " n_samples=1200,\n", + " n_features=2,\n", + " n_informative=2,\n", + " n_redundant=0,\n", + " n_repeated=0,\n", + " n_classes=2,\n", + " n_clusters_per_class=1,\n", + " class_sep=1.35,\n", + " flip_y=0.03,\n", + " random_state=11,\n", + ")\n", + "\n", + "XA_train, XA_dev, yA_train, yA_dev = train_test_split(\n", + " XA, yA,\n", + " train_size=1000,\n", + " test_size=200,\n", + " stratify=yA,\n", + " random_state=101,\n", + ")\n", + "\n", + "# Dataset B: regresión no lineal de orden 5, 2 features\n", + "XB = rng.uniform(-1.6, 1.6, size=(1200, 2))\n", + "x1, x2 = XB[:, 0], XB[:, 1]\n", + "noise = rng.normal(0, 0.35, size=1200)\n", + "\n", + "yB = (\n", + " 0.55 * x1**5\n", + " - 0.45 * x2**5\n", + " + 0.80 * x1**3\n", + " - 0.65 * x2**2\n", + " + 0.50 * x1**2 * x2\n", + " - 0.35 * x1 * x2**3\n", + " + 0.25 * x1\n", + " + noise\n", + ")\n", + "\n", + "XB_train, XB_dev, yB_train, yB_dev = train_test_split(\n", + " XB, yB,\n", + " train_size=1000,\n", + " test_size=200,\n", + " random_state=202,\n", + ")\n", + "\n", + "# Dataset C: clasificación multiclase con 4 clases, 2 features\n", + "XC, yC = make_classification(\n", + " n_samples=1200,\n", + " n_features=2,\n", + " n_informative=2,\n", + " n_redundant=0,\n", + " n_repeated=0,\n", + " n_classes=4,\n", + " n_clusters_per_class=1,\n", + " class_sep=1.45,\n", + " flip_y=0.04,\n", + " random_state=33,\n", + ")\n", + "\n", + "XC_train, XC_dev, yC_train, yC_dev = train_test_split(\n", + " XC, yC,\n", + " train_size=1000,\n", + " test_size=200,\n", + " stratify=yC,\n", + " random_state=303,\n", + ")\n", + "\n", + "# Dataset D: clasificación multilabel con 4 clases, 2 features\n", + "XD, yD = make_multilabel_classification(\n", + " n_samples=1200,\n", + " n_features=2,\n", + " n_classes=4,\n", + " n_labels=2,\n", + " allow_unlabeled=False,\n", + " random_state=44,\n", + ")\n", + "\n", + "XD = XD.astype(np.float64) + rng.normal(0, 0.05, size=XD.shape)\n", + "strat_D = yD.sum(axis=1)\n", + "\n", + "XD_train, XD_dev, yD_train, yD_dev = train_test_split(\n", + " XD, yD,\n", + " train_size=1000,\n", + " test_size=200,\n", + " stratify=strat_D,\n", + " random_state=404,\n", + ")\n", + "\n", + "def save_csv(prefix, X_train, X_dev, y_train, y_dev, multilabel=False):\n", + " if multilabel:\n", + " train_df = pd.DataFrame(X_train, columns=[\"x1\", \"x2\"])\n", + " dev_df = pd.DataFrame(X_dev, columns=[\"x1\", \"x2\"])\n", + " for k in range(y_train.shape[1]):\n", + " train_df[f\"y{k}\"] = y_train[:, k].astype(int)\n", + " dev_df[f\"y{k}\"] = y_dev[:, k].astype(int)\n", + " else:\n", + " train_df = pd.DataFrame(X_train, columns=[\"x1\", \"x2\"])\n", + " dev_df = pd.DataFrame(X_dev, columns=[\"x1\", \"x2\"])\n", + " train_df[\"y\"] = y_train\n", + " dev_df[\"y\"] = y_dev\n", + "\n", + " train_df.to_csv(DATA_DIR / f\"dataset_{prefix}_train.csv\", index=False)\n", + " dev_df.to_csv(DATA_DIR / f\"dataset_{prefix}_dev.csv\", index=False)\n", + "\n", + "save_csv(\"A\", XA_train, XA_dev, yA_train, yA_dev)\n", + "save_csv(\"B\", XB_train, XB_dev, yB_train, yB_dev)\n", + "save_csv(\"C\", XC_train, XC_dev, yC_train, yC_dev)\n", + "save_csv(\"D\", XD_train, XD_dev, yD_train, yD_dev, multilabel=True)\n", + "\n", + "for path in sorted(DATA_DIR.glob(\"*.csv\")):\n", + " print(path, pd.read_csv(path).shape)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ya7JlUdwCFL0", + "metadata": { + "id": "ya7JlUdwCFL0" + }, + "source": [ + "# 2. Verosimilitud, máxima verosimilitud y KL divergence\n", + "\n", + "La verosimilitud interpreta la probabilidad de los datos observados como una función de los parámetros.\n", + "\n", + "Si tenemos datos:\n", + "\n", + "$$\n", + "\\mathcal{D}=\\{x_i\\}_{i=1}^{N}\n", + "$$\n", + "\n", + "y un modelo paramétrico:\n", + "\n", + "$$\n", + "p(x;\\theta)\n", + "$$\n", + "\n", + "la verosimilitud es:\n", + "\n", + "$$\n", + "L(\\theta)=p(\\mathcal{D};\\theta)\n", + "$$\n", + "\n", + "Si los datos son independientes:\n", + "\n", + "$$\n", + "L(\\theta)=\\prod_{i=1}^{N}p(x_i;\\theta)\n", + "$$\n", + "\n", + "La log-verosimilitud es:\n", + "\n", + "$$\n", + "\\ell(\\theta)=\\sum_{i=1}^{N}\\log p(x_i;\\theta)\n", + "$$\n", + "\n", + "El estimador de máxima verosimilitud busca:\n", + "\n", + "$$\n", + "\\hat{\\theta}_{MLE}=\\arg\\max_{\\theta}\\ell(\\theta)\n", + "$$\n", + "\n", + "En el ejemplo siguiente se genera una muestra desde una distribución Bernoulli con parámetro verdadero \\(p^\\star\\). Luego se evalúan varios valores candidatos de \\(p\\). La log-verosimilitud se maximiza cerca del parámetro empírico de la muestra. La divergencia KL entre la distribución empírica y la distribución candidata se minimiza en el mismo punto.\n", + "\n", + "La KL divergence entre dos Bernoulli \\(q\\) y \\(p\\) es:\n", + "\n", + "$$\n", + "D_{KL}(q||p)=q\\log\\frac{q}{p}+(1-q)\\log\\frac{1-q}{1-p}\n", + "$$\n", + "\n", + "Importante: la KL no se maximiza en los parámetros verdaderos. La KL se minimiza cuando ambas distribuciones coinciden.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "qZQ2TeIICFL0", + "metadata": { + "id": "qZQ2TeIICFL0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p verdadero: 0.72\n", + "p empírico: 0.722\n", + "p que maximiza log-verosimilitud en la grilla: 0.724070351758794\n", + "p que minimiza KL en la grilla: 0.724070351758794\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(123)\n", + "\n", + "p_true = 0.72\n", + "n = 1000\n", + "sample = rng.binomial(n=1, p=p_true, size=n)\n", + "\n", + "p_empirical = sample.mean()\n", + "grid = np.linspace(0.01, 0.99, 200)\n", + "\n", + "def bernoulli_log_likelihood(sample, p):\n", + " return np.sum(sample * np.log(p) + (1 - sample) * np.log(1 - p))\n", + "\n", + "def kl_bernoulli(q, p):\n", + " return q * np.log(q / p) + (1 - q) * np.log((1 - q) / (1 - p))\n", + "\n", + "log_likelihoods = np.array([bernoulli_log_likelihood(sample, p) for p in grid])\n", + "kl_values = np.array([kl_bernoulli(p_empirical, p) for p in grid])\n", + "\n", + "p_mle_grid = grid[np.argmax(log_likelihoods)]\n", + "p_kl_min_grid = grid[np.argmin(kl_values)]\n", + "\n", + "print(\"p verdadero:\", p_true)\n", + "print(\"p empírico:\", p_empirical)\n", + "print(\"p que maximiza log-verosimilitud en la grilla:\", p_mle_grid)\n", + "print(\"p que minimiza KL en la grilla:\", p_kl_min_grid)\n", + "\n", + "plt.figure(figsize=(7, 4))\n", + "plt.plot(grid, log_likelihoods)\n", + "plt.axvline(p_true, linestyle=\"--\", label=\"p verdadero\")\n", + "plt.axvline(p_empirical, linestyle=\":\", label=\"p empírico\")\n", + "plt.title(\"Log-verosimilitud Bernoulli\")\n", + "plt.xlabel(\"p candidato\")\n", + "plt.ylabel(\"log L(p)\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(7, 4))\n", + "plt.plot(grid, kl_values)\n", + "plt.axvline(p_true, linestyle=\"--\", label=\"p verdadero\")\n", + "plt.axvline(p_empirical, linestyle=\":\", label=\"p empírico\")\n", + "plt.title(\"KL entre distribución empírica y Bernoulli candidata\")\n", + "plt.xlabel(\"p candidato\")\n", + "plt.ylabel(\"D_KL(q || p)\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "iF4AWjZpCFL0", + "metadata": { + "id": "iF4AWjZpCFL0" + }, + "source": [ + "# 3. Funciones auxiliares de preprocesamiento\n", + "\n", + "En este laboratorio se pide practicar dos transformaciones:\n", + "\n", + "1. **Normalización min-max sobre todo el dataset**: se ajusta con train + dev.\n", + "2. **Estandarización sobre train normalizado**: la media y desviación se ajustan solo sobre train normalizado y luego se aplican a dev.\n", + "\n", + "La normalización min-max es:\n", + "\n", + "$$\n", + "x'=\\frac{x-x_{min}}{x_{max}-x_{min}}\n", + "$$\n", + "\n", + "La estandarización es:\n", + "\n", + "$$\n", + "z=\\frac{x'-\\mu_{train}}{\\sigma_{train}}\n", + "$$\n", + "\n", + "En proyectos reales, usualmente se evita ajustar cualquier transformación con dev o test. Aquí se pide explícitamente normalizar sobre todo el dataset para fines didácticos y luego estandarizar usando solo train.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "aaKMn64hCFL0", + "metadata": { + "id": "aaKMn64hCFL0" + }, + "outputs": [], + "source": [ + "def normalize_all_then_standardize_train(train_df, dev_df, feature_cols=(\"x1\", \"x2\")):\n", + " X_train = train_df[list(feature_cols)].to_numpy(dtype=np.float32)\n", + " X_dev = dev_df[list(feature_cols)].to_numpy(dtype=np.float32)\n", + "\n", + " all_X = np.vstack([X_train, X_dev])\n", + " normalizer = MinMaxScaler()\n", + " normalizer.fit(all_X)\n", + "\n", + " X_train_norm = normalizer.transform(X_train)\n", + " X_dev_norm = normalizer.transform(X_dev)\n", + "\n", + " standardizer = StandardScaler()\n", + " standardizer.fit(X_train_norm)\n", + "\n", + " X_train_std = standardizer.transform(X_train_norm).astype(np.float32)\n", + " X_dev_std = standardizer.transform(X_dev_norm).astype(np.float32)\n", + "\n", + " return X_train_std, X_dev_std, normalizer, standardizer\n", + "\n", + "\n", + "def make_loader(X, y, batch_size=64, shuffle=True):\n", + " X_tensor = torch.tensor(X, dtype=torch.float32)\n", + "\n", + " if y.dtype.kind in {\"i\", \"u\"}:\n", + " y_tensor = torch.tensor(y, dtype=torch.long)\n", + " else:\n", + " y_tensor = torch.tensor(y, dtype=torch.float32)\n", + "\n", + " ds = TensorDataset(X_tensor, y_tensor)\n", + " return DataLoader(ds, batch_size=batch_size, shuffle=shuffle)\n" + ] + }, + { + "cell_type": "markdown", + "id": "yVkFgG64CFL0", + "metadata": { + "id": "yVkFgG64CFL0" + }, + "source": [ + "# 4. Ejemplo completo: Dataset A, clasificación binaria\n", + "\n", + "En este ejemplo se implementa el flujo completo para clasificación binaria:\n", + "\n", + "1. visualizar el dataset;\n", + "2. preprocesar las entradas;\n", + "3. crear una red neuronal de 3 capas lineales: \\(2 \\rightarrow 8 \\rightarrow 8 \\rightarrow 1\\);\n", + "4. entrenar la red;\n", + "5. medir métricas apropiadas en dev.\n", + "\n", + "Para clasificación binaria usamos un logit de salida y `BCEWithLogitsLoss`.\n", + "\n", + "La probabilidad se obtiene después con:\n", + "\n", + "$$\n", + "\\hat{p}=\\sigma(z)\n", + "$$\n", + "\n", + "donde \\(z\\) es el logit.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "Naq5i_fDCFL0", + "metadata": { + "id": "Naq5i_fDCFL0" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "A_train = pd.read_csv(DATA_DIR / \"dataset_A_train.csv\")\n", + "A_dev = pd.read_csv(DATA_DIR / \"dataset_A_dev.csv\")\n", + "\n", + "plt.figure(figsize=(6, 5))\n", + "plt.scatter(A_train[\"x1\"], A_train[\"x2\"], c=A_train[\"y\"], s=16, alpha=0.75)\n", + "plt.title(\"Dataset A: clasificación binaria\")\n", + "plt.xlabel(\"x1\")\n", + "plt.ylabel(\"x2\")\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "X_A_train, X_A_dev, norm_A, std_A = normalize_all_then_standardize_train(A_train, A_dev)\n", + "y_A_train = A_train[\"y\"].to_numpy(dtype=np.float32).reshape(-1, 1)\n", + "y_A_dev = A_dev[\"y\"].to_numpy(dtype=np.float32).reshape(-1, 1)\n", + "\n", + "train_A_loader = DataLoader(\n", + " TensorDataset(torch.tensor(X_A_train), torch.tensor(y_A_train)),\n", + " batch_size=64,\n", + " shuffle=True,\n", + ")\n", + "dev_A_loader = DataLoader(\n", + " TensorDataset(torch.tensor(X_A_dev), torch.tensor(y_A_dev)),\n", + " batch_size=256,\n", + " shuffle=False,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2BKTHnbuCFL1", + "metadata": { + "id": "2BKTHnbuCFL1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'accuracy': 0.98, 'precision': 0.9702970297029703, 'recall': 0.98989898989899, 'f1': 0.98, 'confusion_matrix': array([[98, 3],\n", + " [ 1, 98]])}\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "class BinaryMLP(nn.Module):\n", + " def __init__(self, input_dim=2, hidden_dim=8, output_dim=1):\n", + " super().__init__()\n", + " self.net = nn.Sequential(\n", + " nn.Linear(input_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim, output_dim),\n", + " )\n", + "\n", + " def forward(self, x):\n", + " return self.net(x)\n", + "\n", + "\n", + "def train_binary_model(model, train_loader, epochs=100, lr=1e-2):\n", + " criterion = nn.BCEWithLogitsLoss()\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n", + " history = []\n", + "\n", + " for epoch in range(epochs):\n", + " model.train()\n", + " total_loss = 0.0\n", + " total_n = 0\n", + "\n", + " for xb, yb in train_loader:\n", + " logits = model(xb)\n", + " loss = criterion(logits, yb)\n", + "\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " total_loss += loss.item() * xb.size(0)\n", + " total_n += xb.size(0)\n", + "\n", + " history.append(total_loss / total_n)\n", + "\n", + " return history\n", + "\n", + "\n", + "def evaluate_binary_model(model, dev_loader, threshold=0.5):\n", + " model.eval()\n", + " y_true = []\n", + " y_pred = []\n", + "\n", + " with torch.no_grad():\n", + " for xb, yb in dev_loader:\n", + " logits = model(xb)\n", + " probs = torch.sigmoid(logits)\n", + " preds = (probs >= threshold).int()\n", + "\n", + " y_true.append(yb.cpu().numpy())\n", + " y_pred.append(preds.cpu().numpy())\n", + "\n", + " y_true = np.vstack(y_true).reshape(-1)\n", + " y_pred = np.vstack(y_pred).reshape(-1)\n", + "\n", + " return {\n", + " \"accuracy\": accuracy_score(y_true, y_pred),\n", + " \"precision\": precision_score(y_true, y_pred, zero_division=0),\n", + " \"recall\": recall_score(y_true, y_pred, zero_division=0),\n", + " \"f1\": f1_score(y_true, y_pred, zero_division=0),\n", + " \"confusion_matrix\": confusion_matrix(y_true, y_pred),\n", + " }\n", + "\n", + "\n", + "binary_model = BinaryMLP()\n", + "history_A = train_binary_model(binary_model, train_A_loader, epochs=100, lr=1e-2)\n", + "metrics_A = evaluate_binary_model(binary_model, dev_A_loader)\n", + "\n", + "print(metrics_A)\n", + "\n", + "plt.figure(figsize=(6, 4))\n", + "plt.plot(history_A)\n", + "plt.title(\"Dataset A: pérdida de entrenamiento\")\n", + "plt.xlabel(\"epoch\")\n", + "plt.ylabel(\"BCEWithLogitsLoss\")\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "Xkv_9YLTCFL1", + "metadata": { + "id": "Xkv_9YLTCFL1" + }, + "source": [ + "# 5. Pruebas públicas y privadas\n", + "\n", + "Las pruebas públicas se ejecutan dentro del notebook para dar retroalimentación al estudiante.\n", + "\n", + "La calificación final se distribuye así:\n", + "\n", + "| Tarea | Dataset | Peso |\n", + "|---|---|---:|\n", + "| Tarea 1 | Dataset B, regresión | 30% |\n", + "| Tarea 2 | Dataset C, multiclase | 30% |\n", + "| Tarea 3 | Dataset D, multilabel | 40% |\n", + "\n", + "Dentro de cada tarea:\n", + "\n", + "| Subtarea | Peso interno |\n", + "|---|---:|\n", + "| Modelo MLP | 40% |\n", + "| Función de entrenamiento | 30% |\n", + "| Función de evaluación | 30% |\n" + ] + }, + { + "cell_type": "markdown", + "id": "25jL4cTRCFL1", + "metadata": { + "id": "25jL4cTRCFL1" + }, + "source": [ + "# Tarea 1 — Dataset B: regresión no lineal\n", + "\n", + "Esta tarea utiliza el dataset B.\n", + "\n", + "## Actividades no calificadas\n", + "\n", + "1. Visualizar el dataset B.\n", + "2. Normalizar las entradas sobre todo el dataset.\n", + "3. Estandarizar las entradas usando las estadísticas de train normalizado.\n", + "\n", + "## Actividades calificadas\n", + "\n", + "Debe implementar:\n", + "\n", + "1. `RegressionMLP`: red MLP para regresión con arquitectura \\(2 \\rightarrow 8 \\rightarrow 8 \\rightarrow 1\\), ReLU en las capas ocultas y salida lineal.\n", + "2. `train_regression_model`: función que entrena la red durante 100 epochs por defecto.\n", + "3. `evaluate_regression_model`: función que retorna métricas apropiadas.\n", + "\n", + "Métricas esperadas:\n", + "\n", + "- `mse`;\n", + "- `rmse`;\n", + "- `mae`;\n", + "- `r2`.\n", + "\n", + "Para regresión con error normal, MSE corresponde a la log-verosimilitud negativa, salvo constantes y escala:\n", + "\n", + "$$\n", + "\\mathcal{L}_{MSE}\n", + "=\n", + "\\frac{1}{N}\n", + "\\sum_{i=1}^{N}\n", + "(y_i-\\hat{y}_i)^2\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "Vxb9qmH7CFL1", + "metadata": { + "id": "Vxb9qmH7CFL1" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_B_train: (1000, 2)\n", + "y_B_train: (1000, 1)\n" + ] + } + ], + "source": [ + "B_train = pd.read_csv(DATA_DIR / \"dataset_B_train.csv\")\n", + "B_dev = pd.read_csv(DATA_DIR / \"dataset_B_dev.csv\")\n", + "\n", + "# Non-graded: visualización\n", + "plt.figure(figsize=(6, 5))\n", + "plt.scatter(B_train[\"x1\"], B_train[\"x2\"], c=B_train[\"y\"], s=16, alpha=0.75)\n", + "plt.title(\"Dataset B: regresión no lineal\")\n", + "plt.xlabel(\"x1\")\n", + "plt.ylabel(\"x2\")\n", + "plt.colorbar(label=\"y\")\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# Non-graded: normalización y estandarización\n", + "X_B_train, X_B_dev, norm_B, std_B = normalize_all_then_standardize_train(B_train, B_dev)\n", + "y_B_train = B_train[\"y\"].to_numpy(dtype=np.float32).reshape(-1, 1)\n", + "y_B_dev = B_dev[\"y\"].to_numpy(dtype=np.float32).reshape(-1, 1)\n", + "\n", + "train_B_loader = DataLoader(\n", + " TensorDataset(torch.tensor(X_B_train), torch.tensor(y_B_train)),\n", + " batch_size=64,\n", + " shuffle=True,\n", + ")\n", + "dev_B_loader = DataLoader(\n", + " TensorDataset(torch.tensor(X_B_dev), torch.tensor(y_B_dev)),\n", + " batch_size=256,\n", + " shuffle=False,\n", + ")\n", + "\n", + "print(\"X_B_train:\", X_B_train.shape)\n", + "print(\"y_B_train:\", y_B_train.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8cclzgJpCFL1", + "metadata": { + "id": "8cclzgJpCFL1" + }, + "outputs": [], + "source": [ + "# GRADED: Tarea 1\n", + "\n", + "class RegressionMLP(nn.Module):\n", + " def __init__(self, input_dim=2, hidden_dim=8, output_dim=1):\n", + " super().__init__()\n", + "\n", + " # START CODE HERE\n", + " # Arquitectura 2 -> 8 -> 8 -> 1.\n", + " # Las activaciones se registran como módulos nn.ReLU (no F.relu) para que\n", + " # aparezcan en model.modules(). La capa de salida queda lineal: en regresión\n", + " # el modelo predice directamente la media condicional de y.\n", + " self.net = nn.Sequential(\n", + " nn.Linear(input_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim, output_dim),\n", + " )\n", + " # END CODE HERE\n", + "\n", + " def forward(self, x):\n", + " # START CODE HERE\n", + " return self.net(x)\n", + " # END CODE HERE\n", + "\n", + "\n", + "def train_regression_model(model, train_loader, epochs=100, lr=1e-2):\n", + " \"\"\"\n", + " Entrena un modelo de regresión usando MSELoss.\n", + "\n", + " Debe retornar una lista con la pérdida promedio por epoch.\n", + " \"\"\"\n", + " # START CODE HERE\n", + " # MSE es la log-verosimilitud negativa bajo error gaussiano homocedástico,\n", + " # salvo constantes y escala.\n", + " criterion = nn.MSELoss()\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n", + " history = []\n", + "\n", + " for epoch in range(epochs):\n", + " model.train()\n", + " total_loss = 0.0\n", + " total_n = 0\n", + "\n", + " for xb, yb in train_loader:\n", + " preds = model(xb)\n", + " loss = criterion(preds, yb)\n", + "\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # Se pondera por el tamaño del batch para que el último batch,\n", + " # que puede ser más pequeño, no distorsione el promedio.\n", + " total_loss += loss.item() * xb.size(0)\n", + " total_n += xb.size(0)\n", + "\n", + " history.append(total_loss / total_n)\n", + "\n", + " return history\n", + " # END CODE HERE\n", + "\n", + "\n", + "def evaluate_regression_model(model, dev_loader):\n", + " \"\"\"\n", + " Evalúa el modelo de regresión.\n", + "\n", + " Debe retornar un diccionario con:\n", + " mse, rmse, mae, r2\n", + " \"\"\"\n", + " # START CODE HERE\n", + " model.eval()\n", + " y_true = []\n", + " y_pred = []\n", + "\n", + " with torch.no_grad():\n", + " for xb, yb in dev_loader:\n", + " preds = model(xb)\n", + "\n", + " y_true.append(yb.cpu().numpy())\n", + " y_pred.append(preds.cpu().numpy())\n", + "\n", + " y_true = np.vstack(y_true).reshape(-1)\n", + " y_pred = np.vstack(y_pred).reshape(-1)\n", + "\n", + " # El RMSE se calcula con np.sqrt: el argumento squared=False de\n", + " # mean_squared_error fue eliminado en scikit-learn 1.6.\n", + " mse = mean_squared_error(y_true, y_pred)\n", + "\n", + " return {\n", + " \"mse\": float(mse),\n", + " \"rmse\": float(np.sqrt(mse)),\n", + " \"mae\": float(mean_absolute_error(y_true, y_pred)),\n", + " \"r2\": float(r2_score(y_true, y_pred)),\n", + " }\n", + " # END CODE HERE\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "xsjmG3LGCFL1", + "metadata": { + "id": "xsjmG3LGCFL1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tarea 1 public tests passed.\n" + ] + } + ], + "source": [ + "# Public test: ejecutar después de implementar Tarea 1.\n", + "public_tests.test_tarea1(globals())\n" + ] + }, + { + "cell_type": "markdown", + "id": "Jb5zKzbwCFL1", + "metadata": { + "id": "Jb5zKzbwCFL1" + }, + "source": [ + "# Tarea 2 — Dataset C: clasificación multiclase\n", + "\n", + "Esta tarea utiliza el dataset C.\n", + "\n", + "## Actividades no calificadas\n", + "\n", + "1. Visualizar el dataset C.\n", + "2. Normalizar las entradas sobre todo el dataset.\n", + "3. Estandarizar las entradas usando las estadísticas de train normalizado.\n", + "\n", + "## Actividades calificadas\n", + "\n", + "Debe implementar:\n", + "\n", + "1. `MulticlassMLP`: red MLP para clasificación multiclase con arquitectura \\(2 \\rightarrow 8 \\rightarrow 8 \\rightarrow 4\\), ReLU en las capas ocultas y salida de 4 logits.\n", + "2. `train_multiclass_model`: función que entrena la red durante 100 epochs por defecto.\n", + "3. `evaluate_multiclass_model`: función que retorna métricas apropiadas.\n", + "\n", + "Métricas esperadas:\n", + "\n", + "- `accuracy`;\n", + "- `macro_f1`;\n", + "- `confusion_matrix`.\n", + "\n", + "Para clasificación multiclase se usa cross-entropy categórica:\n", + "\n", + "$$\n", + "\\mathcal{L}_{CE}\n", + "=\n", + "-\\frac{1}{N}\n", + "\\sum_{i=1}^{N}\n", + "\\log p(y_i|\\mathbf{x}_i)\n", + "$$\n", + "\n", + "En PyTorch, `CrossEntropyLoss` recibe logits directamente.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "Mi-vC9ETCFL1", + "metadata": { + "id": "Mi-vC9ETCFL1" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_C_train: (1000, 2)\n", + "y_C_train: (1000,)\n" + ] + } + ], + "source": [ + "C_train = pd.read_csv(DATA_DIR / \"dataset_C_train.csv\")\n", + "C_dev = pd.read_csv(DATA_DIR / \"dataset_C_dev.csv\")\n", + "\n", + "# Non-graded: visualización\n", + "plt.figure(figsize=(6, 5))\n", + "plt.scatter(C_train[\"x1\"], C_train[\"x2\"], c=C_train[\"y\"], s=16, alpha=0.75)\n", + "plt.title(\"Dataset C: clasificación multiclase\")\n", + "plt.xlabel(\"x1\")\n", + "plt.ylabel(\"x2\")\n", + "plt.colorbar(label=\"clase\")\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# Non-graded: normalización y estandarización\n", + "X_C_train, X_C_dev, norm_C, std_C = normalize_all_then_standardize_train(C_train, C_dev)\n", + "y_C_train = C_train[\"y\"].to_numpy(dtype=np.int64)\n", + "y_C_dev = C_dev[\"y\"].to_numpy(dtype=np.int64)\n", + "\n", + "train_C_loader = DataLoader(\n", + " TensorDataset(torch.tensor(X_C_train), torch.tensor(y_C_train)),\n", + " batch_size=64,\n", + " shuffle=True,\n", + ")\n", + "dev_C_loader = DataLoader(\n", + " TensorDataset(torch.tensor(X_C_dev), torch.tensor(y_C_dev)),\n", + " batch_size=256,\n", + " shuffle=False,\n", + ")\n", + "\n", + "print(\"X_C_train:\", X_C_train.shape)\n", + "print(\"y_C_train:\", y_C_train.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ieesh8NxCFL1", + "metadata": { + "id": "ieesh8NxCFL1" + }, + "outputs": [], + "source": [ + "# GRADED: Tarea 2\n", + "\n", + "class MulticlassMLP(nn.Module):\n", + " def __init__(self, input_dim=2, hidden_dim=8, output_dim=4):\n", + " super().__init__()\n", + "\n", + " # START CODE HERE\n", + " # Arquitectura 2 -> 8 -> 8 -> 4. La salida son logits crudos, sin softmax:\n", + " # CrossEntropyLoss ya aplica log_softmax internamente, y aplicarlo dos veces\n", + " # aplanaría los gradientes.\n", + " self.net = nn.Sequential(\n", + " nn.Linear(input_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim, output_dim),\n", + " )\n", + " # END CODE HERE\n", + "\n", + " def forward(self, x):\n", + " # START CODE HERE\n", + " return self.net(x)\n", + " # END CODE HERE\n", + "\n", + "\n", + "def train_multiclass_model(model, train_loader, epochs=100, lr=1e-2):\n", + " \"\"\"\n", + " Entrena un modelo de clasificación multiclase usando CrossEntropyLoss.\n", + "\n", + " Debe retornar una lista con la pérdida promedio por epoch.\n", + " \"\"\"\n", + " # START CODE HERE\n", + " # CrossEntropyLoss espera logits (N, C) y etiquetas enteras (N,) de tipo long.\n", + " criterion = nn.CrossEntropyLoss()\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n", + " history = []\n", + "\n", + " for epoch in range(epochs):\n", + " model.train()\n", + " total_loss = 0.0\n", + " total_n = 0\n", + "\n", + " for xb, yb in train_loader:\n", + " logits = model(xb)\n", + " loss = criterion(logits, yb)\n", + "\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " total_loss += loss.item() * xb.size(0)\n", + " total_n += xb.size(0)\n", + "\n", + " history.append(total_loss / total_n)\n", + "\n", + " return history\n", + " # END CODE HERE\n", + "\n", + "\n", + "def evaluate_multiclass_model(model, dev_loader):\n", + " \"\"\"\n", + " Evalúa el modelo multiclase.\n", + "\n", + " Debe retornar un diccionario con:\n", + " accuracy, macro_f1, confusion_matrix\n", + " \"\"\"\n", + " # START CODE HERE\n", + " model.eval()\n", + " y_true = []\n", + " y_pred = []\n", + "\n", + " with torch.no_grad():\n", + " for xb, yb in dev_loader:\n", + " logits = model(xb)\n", + " # argmax sobre los logits equivale a argmax sobre las probabilidades,\n", + " # porque softmax es monótona: no hace falta calcularla.\n", + " preds = logits.argmax(dim=1)\n", + "\n", + " y_true.append(yb.cpu().numpy())\n", + " y_pred.append(preds.cpu().numpy())\n", + "\n", + " y_true = np.concatenate(y_true)\n", + " y_pred = np.concatenate(y_pred)\n", + "\n", + " return {\n", + " \"accuracy\": float(accuracy_score(y_true, y_pred)),\n", + " # macro promedia el F1 por clase sin ponderar por soporte, así que\n", + " # penaliza el desempeño pobre en las clases minoritarias.\n", + " \"macro_f1\": float(f1_score(y_true, y_pred, average=\"macro\", zero_division=0)),\n", + " \"confusion_matrix\": confusion_matrix(y_true, y_pred),\n", + " }\n", + " # END CODE HERE\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "rWCbdcYZCFL1", + "metadata": { + "id": "rWCbdcYZCFL1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tarea 2 public tests passed.\n" + ] + } + ], + "source": [ + "# Public test: ejecutar después de implementar Tarea 2.\n", + "public_tests.test_tarea2(globals())\n" + ] + }, + { + "cell_type": "markdown", + "id": "DSM5iYJeCFL1", + "metadata": { + "id": "DSM5iYJeCFL1" + }, + "source": [ + "# Tarea 3 — Dataset D: clasificación multilabel\n", + "\n", + "Esta tarea utiliza el dataset D.\n", + "\n", + "## Actividades no calificadas\n", + "\n", + "1. Visualizar el dataset D.\n", + "2. Normalizar las entradas sobre todo el dataset.\n", + "3. Estandarizar las entradas usando las estadísticas de train normalizado.\n", + "\n", + "## Actividades calificadas\n", + "\n", + "Debe implementar:\n", + "\n", + "1. `MultilabelMLP`: red MLP para clasificación multilabel con arquitectura \\(2 \\rightarrow 8 \\rightarrow 8 \\rightarrow 4\\), ReLU en las capas ocultas y salida de 4 logits.\n", + "2. `train_multilabel_model`: función que entrena la red durante 100 epochs por defecto.\n", + "3. `evaluate_multilabel_model`: función que retorna métricas apropiadas.\n", + "\n", + "Métricas esperadas:\n", + "\n", + "- `subset_accuracy`;\n", + "- `micro_f1`;\n", + "- `macro_f1`.\n", + "\n", + "En clasificación multilabel, cada etiqueta se modela como una Bernoulli independiente. Por eso se usa `BCEWithLogitsLoss` con 4 logits de salida.\n", + "\n", + "La pérdida por etiqueta es:\n", + "\n", + "$$\n", + "\\mathcal{L}_{BCE}\n", + "=\n", + "-\\frac{1}{N}\n", + "\\sum_{i=1}^{N}\n", + "\\left[\n", + "y_i\\log(\\hat{y}_i)\n", + "+\n", + "(1-y_i)\\log(1-\\hat{y}_i)\n", + "\\right]\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "vLhapKefCFL2", + "metadata": { + "id": "vLhapKefCFL2" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_D_train: (1000, 2)\n", + "y_D_train: (1000, 4)\n" + ] + } + ], + "source": [ + "D_train = pd.read_csv(DATA_DIR / \"dataset_D_train.csv\")\n", + "D_dev = pd.read_csv(DATA_DIR / \"dataset_D_dev.csv\")\n", + "\n", + "# Non-graded: visualización.\n", + "# Para visualizar multilabel en 2D, coloreamos por el número de etiquetas activas.\n", + "label_cols = [\"y0\", \"y1\", \"y2\", \"y3\"]\n", + "D_train[\"label_count\"] = D_train[label_cols].sum(axis=1)\n", + "\n", + "plt.figure(figsize=(6, 5))\n", + "plt.scatter(D_train[\"x1\"], D_train[\"x2\"], c=D_train[\"label_count\"], s=16, alpha=0.75)\n", + "plt.title(\"Dataset D: clasificación multilabel\")\n", + "plt.xlabel(\"x1\")\n", + "plt.ylabel(\"x2\")\n", + "plt.colorbar(label=\"número de etiquetas activas\")\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# Non-graded: normalización y estandarización\n", + "X_D_train, X_D_dev, norm_D, std_D = normalize_all_then_standardize_train(D_train, D_dev)\n", + "y_D_train = D_train[label_cols].to_numpy(dtype=np.float32)\n", + "y_D_dev = D_dev[label_cols].to_numpy(dtype=np.float32)\n", + "\n", + "train_D_loader = DataLoader(\n", + " TensorDataset(torch.tensor(X_D_train), torch.tensor(y_D_train)),\n", + " batch_size=64,\n", + " shuffle=True,\n", + ")\n", + "dev_D_loader = DataLoader(\n", + " TensorDataset(torch.tensor(X_D_dev), torch.tensor(y_D_dev)),\n", + " batch_size=256,\n", + " shuffle=False,\n", + ")\n", + "\n", + "print(\"X_D_train:\", X_D_train.shape)\n", + "print(\"y_D_train:\", y_D_train.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "LNB27jPLCFL2", + "metadata": { + "id": "LNB27jPLCFL2" + }, + "outputs": [], + "source": [ + "# GRADED: Tarea 3\n", + "\n", + "class MultilabelMLP(nn.Module):\n", + " def __init__(self, input_dim=2, hidden_dim=8, output_dim=4):\n", + " super().__init__()\n", + "\n", + " # START CODE HERE\n", + " # Arquitectura 2 -> 8 -> 8 -> 4. Cada una de las 4 salidas es el logit de una\n", + " # Bernoulli independiente, así que la sigmoide se aplica después (dentro de\n", + " # BCEWithLogitsLoss al entrenar, y de forma explícita al evaluar).\n", + " self.net = nn.Sequential(\n", + " nn.Linear(input_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim, output_dim),\n", + " )\n", + " # END CODE HERE\n", + "\n", + " def forward(self, x):\n", + " # START CODE HERE\n", + " return self.net(x)\n", + " # END CODE HERE\n", + "\n", + "\n", + "def train_multilabel_model(model, train_loader, epochs=100, lr=1e-2):\n", + " \"\"\"\n", + " Entrena un modelo de clasificación multilabel usando BCEWithLogitsLoss.\n", + "\n", + " Debe retornar una lista con la pérdida promedio por epoch.\n", + " \"\"\"\n", + " # START CODE HERE\n", + " # BCEWithLogitsLoss combina sigmoide y BCE en una sola operación estable\n", + " # numéricamente (log-sum-exp), y promedia sobre las 4 etiquetas.\n", + " criterion = nn.BCEWithLogitsLoss()\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n", + " history = []\n", + "\n", + " for epoch in range(epochs):\n", + " model.train()\n", + " total_loss = 0.0\n", + " total_n = 0\n", + "\n", + " for xb, yb in train_loader:\n", + " logits = model(xb)\n", + " loss = criterion(logits, yb)\n", + "\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " total_loss += loss.item() * xb.size(0)\n", + " total_n += xb.size(0)\n", + "\n", + " history.append(total_loss / total_n)\n", + "\n", + " return history\n", + " # END CODE HERE\n", + "\n", + "\n", + "def evaluate_multilabel_model(model, dev_loader, threshold=0.5):\n", + " \"\"\"\n", + " Evalúa el modelo multilabel.\n", + "\n", + " Debe retornar un diccionario con:\n", + " subset_accuracy, micro_f1, macro_f1\n", + " \"\"\"\n", + " # START CODE HERE\n", + " model.eval()\n", + " y_true = []\n", + " y_pred = []\n", + "\n", + " with torch.no_grad():\n", + " for xb, yb in dev_loader:\n", + " logits = model(xb)\n", + " probs = torch.sigmoid(logits)\n", + " # Cada etiqueta se decide por separado contra el umbral.\n", + " preds = (probs >= threshold).int()\n", + "\n", + " y_true.append(yb.cpu().numpy())\n", + " y_pred.append(preds.cpu().numpy())\n", + "\n", + " # Ambas matrices se castean a entero: las etiquetas llegan como float32 y\n", + " # sklearn interpretaría una mezcla float/bool como continuous-multioutput.\n", + " y_true = np.vstack(y_true).astype(int)\n", + " y_pred = np.vstack(y_pred).astype(int)\n", + "\n", + " return {\n", + " # Con matrices 2D, accuracy_score exige que las 4 etiquetas de la fila\n", + " # coincidan: es exactamente el subset accuracy (exact match ratio).\n", + " \"subset_accuracy\": float(accuracy_score(y_true, y_pred)),\n", + " # micro agrega TP/FP/FN de todas las etiquetas antes de calcular el F1;\n", + " # macro promedia el F1 de cada etiqueta por separado.\n", + " \"micro_f1\": float(f1_score(y_true, y_pred, average=\"micro\", zero_division=0)),\n", + " \"macro_f1\": float(f1_score(y_true, y_pred, average=\"macro\", zero_division=0)),\n", + " }\n", + " # END CODE HERE\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "XiGclmWpCFL2", + "metadata": { + "id": "XiGclmWpCFL2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tarea 3 public tests passed.\n" + ] + } + ], + "source": [ + "# Public test: ejecutar después de implementar Tarea 3.\n", + "public_tests.test_tarea3(globals())\n" + ] + }, + { + "cell_type": "markdown", + "id": "lERAz931CFL2", + "metadata": { + "id": "lERAz931CFL2" + }, + "source": [ + "# Ejecución de pruebas privadas\n", + "\n", + "En un ambiente real de calificación, el archivo `private.py` no se entrega al estudiante.\n", + "\n", + "Las siguientes líneas deben permanecer comentadas en la versión pública. Cuando el instructor ejecute las pruebas privadas, estas líneas pueden activarse o ejecutarse desde otro script. El archivo privado escribirá un CSV llamado `notas.csv` con columnas:\n", + "\n", + "- `tarea1`;\n", + "- `tarea2`;\n", + "- `tarea3`;\n", + "- `nota final`.\n", + "\n", + "La nota final se calcula como:\n", + "\n", + "$$\n", + "\\text{nota final}\n", + "=\n", + "0.30(tarea1)\n", + "+\n", + "0.30(tarea2)\n", + "+\n", + "0.40(tarea3)\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "ZjJFB_KQCFL2", + "metadata": { + "id": "ZjJFB_KQCFL2" + }, + "source": [ + "# Apéndice: ejemplos computables a mano\n", + "\n", + "## Forward pass de una neurona\n", + "\n", + "Suponga:\n", + "\n", + "$$\n", + "\\mathbf{x}=\n", + "\\begin{bmatrix}\n", + "2\\\\\n", + "-1\n", + "\\end{bmatrix},\n", + "\\quad\n", + "\\mathbf{w}=\n", + "\\begin{bmatrix}\n", + "0.5\\\\\n", + "-2\n", + "\\end{bmatrix},\n", + "\\quad\n", + "b=1\n", + "$$\n", + "\n", + "Entonces:\n", + "\n", + "$$\n", + "z=\\mathbf{w}^T\\mathbf{x}+b\n", + "$$\n", + "\n", + "$$\n", + "z=(0.5)(2)+(-2)(-1)+1=4\n", + "$$\n", + "\n", + "Si usamos ReLU:\n", + "\n", + "$$\n", + "a=\\max(0,4)=4\n", + "$$\n", + "\n", + "## MSE\n", + "\n", + "Si:\n", + "\n", + "$$\n", + "\\mathbf{y}=[3,0,1],\n", + "\\quad\n", + "\\hat{\\mathbf{y}}=[2.5,0.7,1.2]\n", + "$$\n", + "\n", + "entonces:\n", + "\n", + "$$\n", + "MSE=\\frac{(3-2.5)^2+(0-0.7)^2+(1-1.2)^2}{3}\n", + "$$\n", + "\n", + "## BCE\n", + "\n", + "Si \\(y=1\\) y \\(\\hat{p}=0.8\\):\n", + "\n", + "$$\n", + "BCE=-\\log(0.8)\n", + "$$\n", + "\n", + "Si \\(y=0\\) y \\(\\hat{p}=0.8\\):\n", + "\n", + "$$\n", + "BCE=-\\log(1-0.8)\n", + "$$\n", + "\n", + "## Categorical cross-entropy\n", + "\n", + "Si la clase correcta es \\(2\\) y:\n", + "\n", + "$$\n", + "\\mathbf{p}=[0.1,0.7,0.2]\n", + "$$\n", + "\n", + "entonces:\n", + "\n", + "$$\n", + "CE=-\\log(0.2)\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "OQyqSG09CFL2", + "metadata": { + "id": "OQyqSG09CFL2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Forward neurona: 4.0 4.0\n", + "MSE: 0.26\n", + "BCE y=1, p=0.8: 0.2231435513142097\n", + "BCE y=0, p=0.8: 1.6094379124341005\n", + "CE: 1.6094379124341003\n" + ] + } + ], + "source": [ + "# Cálculos rápidos para verificar los ejemplos a mano.\n", + "\n", + "z = 0.5 * 2 + (-2) * (-1) + 1\n", + "relu_z = max(0, z)\n", + "print(\"Forward neurona:\", z, relu_z)\n", + "\n", + "y = np.array([3.0, 0.0, 1.0])\n", + "y_hat = np.array([2.5, 0.7, 1.2])\n", + "mse = np.mean((y - y_hat) ** 2)\n", + "print(\"MSE:\", mse)\n", + "\n", + "print(\"BCE y=1, p=0.8:\", -np.log(0.8))\n", + "print(\"BCE y=0, p=0.8:\", -np.log(1 - 0.8))\n", + "\n", + "p = np.array([0.1, 0.7, 0.2])\n", + "correct_class = 2\n", + "print(\"CE:\", -np.log(p[correct_class]))\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Lab1/public_tests.py b/Lab1/public_tests.py new file mode 100755 index 0000000..5962390 --- /dev/null +++ b/Lab1/public_tests.py @@ -0,0 +1,158 @@ +""" +Public tests for the MLP lab. + +Usage inside the notebook: + import public_tests as public_tests + public_tests.test_tarea1(globals()) + public_tests.test_tarea2(globals()) + public_tests.test_tarea3(globals()) + +These tests are intentionally lightweight. Passing public tests does not guarantee +full credit in the private grader. +""" + +from pathlib import Path +import numpy as np +import pandas as pd +import torch +import torch.nn as nn +from torch.utils.data import TensorDataset, DataLoader +from sklearn.preprocessing import MinMaxScaler, StandardScaler + +DATA_DIR = Path("data") + + +def _require(namespace, name): + assert name in namespace, f"Missing required object: {name}" + return namespace[name] + + +def _preprocess_features(train_df, dev_df, feature_cols=("x1", "x2")): + X_train = train_df[list(feature_cols)].to_numpy(dtype=np.float32) + X_dev = dev_df[list(feature_cols)].to_numpy(dtype=np.float32) + all_X = np.vstack([X_train, X_dev]) + normalizer = MinMaxScaler() + normalizer.fit(all_X) + X_train_norm = normalizer.transform(X_train) + X_dev_norm = normalizer.transform(X_dev) + standardizer = StandardScaler() + standardizer.fit(X_train_norm) + X_train_std = standardizer.transform(X_train_norm).astype(np.float32) + X_dev_std = standardizer.transform(X_dev_norm).astype(np.float32) + return X_train_std, X_dev_std + + +def _loader_regression(batch_size=64): + train_df = pd.read_csv(DATA_DIR / "dataset_B_train.csv") + dev_df = pd.read_csv(DATA_DIR / "dataset_B_dev.csv") + X_train, X_dev = _preprocess_features(train_df, dev_df) + y_train = train_df["y"].to_numpy(dtype=np.float32).reshape(-1, 1) + y_dev = dev_df["y"].to_numpy(dtype=np.float32).reshape(-1, 1) + train_ds = TensorDataset(torch.tensor(X_train), torch.tensor(y_train)) + dev_ds = TensorDataset(torch.tensor(X_dev), torch.tensor(y_dev)) + return DataLoader(train_ds, batch_size=batch_size, shuffle=True), DataLoader(dev_ds, batch_size=256) + + +def _loader_multiclass(batch_size=64): + train_df = pd.read_csv(DATA_DIR / "dataset_C_train.csv") + dev_df = pd.read_csv(DATA_DIR / "dataset_C_dev.csv") + X_train, X_dev = _preprocess_features(train_df, dev_df) + y_train = train_df["y"].to_numpy(dtype=np.int64) + y_dev = dev_df["y"].to_numpy(dtype=np.int64) + train_ds = TensorDataset(torch.tensor(X_train), torch.tensor(y_train)) + dev_ds = TensorDataset(torch.tensor(X_dev), torch.tensor(y_dev)) + return DataLoader(train_ds, batch_size=batch_size, shuffle=True), DataLoader(dev_ds, batch_size=256) + + +def _loader_multilabel(batch_size=64): + train_df = pd.read_csv(DATA_DIR / "dataset_D_train.csv") + dev_df = pd.read_csv(DATA_DIR / "dataset_D_dev.csv") + X_train, X_dev = _preprocess_features(train_df, dev_df) + y_cols = ["y0", "y1", "y2", "y3"] + y_train = train_df[y_cols].to_numpy(dtype=np.float32) + y_dev = dev_df[y_cols].to_numpy(dtype=np.float32) + train_ds = TensorDataset(torch.tensor(X_train), torch.tensor(y_train)) + dev_ds = TensorDataset(torch.tensor(X_dev), torch.tensor(y_dev)) + return DataLoader(train_ds, batch_size=batch_size, shuffle=True), DataLoader(dev_ds, batch_size=256) + + +def _count_linears(model): + return [m for m in model.modules() if isinstance(m, nn.Linear)] + + +def _has_relu(model): + return any(isinstance(m, nn.ReLU) for m in model.modules()) + + +def _assert_three_layer_mlp(model, output_dim): + linears = _count_linears(model) + assert len(linears) == 3, "The model must contain exactly 3 Linear layers." + assert linears[0].in_features == 2, "The first Linear layer must receive 2 input features." + assert linears[0].out_features == 8, "The first hidden layer must have 8 units." + assert linears[1].in_features == 8 and linears[1].out_features == 8, "The second hidden layer must be 8 -> 8." + assert linears[2].in_features == 8 and linears[2].out_features == output_dim, f"The output layer must have {output_dim} units." + assert _has_relu(model), "The model should include ReLU activations." + + +def test_tarea1(namespace): + torch.manual_seed(123) + RegressionMLP = _require(namespace, "RegressionMLP") + train_fn = _require(namespace, "train_regression_model") + eval_fn = _require(namespace, "evaluate_regression_model") + model = RegressionMLP() + assert isinstance(model, nn.Module), "RegressionMLP must be a torch.nn.Module." + _assert_three_layer_mlp(model, output_dim=1) + x = torch.randn(5, 2) + out = model(x) + assert tuple(out.shape) == (5, 1), "RegressionMLP forward output must have shape (batch, 1)." + train_loader, dev_loader = _loader_regression() + _ = train_fn(model, train_loader, epochs=5, lr=1e-2) + metrics = eval_fn(model, dev_loader) + assert isinstance(metrics, dict), "evaluate_regression_model must return a dictionary." + for key in ["mse", "rmse", "mae", "r2"]: + assert key in metrics, f"Missing regression metric: {key}" + assert np.isfinite(metrics[key]), f"Metric {key} must be finite." + print("Tarea 1 public tests passed.") + + +def test_tarea2(namespace): + torch.manual_seed(123) + MulticlassMLP = _require(namespace, "MulticlassMLP") + train_fn = _require(namespace, "train_multiclass_model") + eval_fn = _require(namespace, "evaluate_multiclass_model") + model = MulticlassMLP() + assert isinstance(model, nn.Module), "MulticlassMLP must be a torch.nn.Module." + _assert_three_layer_mlp(model, output_dim=4) + x = torch.randn(5, 2) + out = model(x) + assert tuple(out.shape) == (5, 4), "MulticlassMLP forward output must have shape (batch, 4)." + train_loader, dev_loader = _loader_multiclass() + _ = train_fn(model, train_loader, epochs=5, lr=1e-2) + metrics = eval_fn(model, dev_loader) + assert isinstance(metrics, dict), "evaluate_multiclass_model must return a dictionary." + for key in ["accuracy", "macro_f1", "confusion_matrix"]: + assert key in metrics, f"Missing multiclass metric: {key}" + assert np.isfinite(metrics["accuracy"]), "accuracy must be finite." + assert np.isfinite(metrics["macro_f1"]), "macro_f1 must be finite." + print("Tarea 2 public tests passed.") + + +def test_tarea3(namespace): + torch.manual_seed(123) + MultilabelMLP = _require(namespace, "MultilabelMLP") + train_fn = _require(namespace, "train_multilabel_model") + eval_fn = _require(namespace, "evaluate_multilabel_model") + model = MultilabelMLP() + assert isinstance(model, nn.Module), "MultilabelMLP must be a torch.nn.Module." + _assert_three_layer_mlp(model, output_dim=4) + x = torch.randn(5, 2) + out = model(x) + assert tuple(out.shape) == (5, 4), "MultilabelMLP forward output must have shape (batch, 4)." + train_loader, dev_loader = _loader_multilabel() + _ = train_fn(model, train_loader, epochs=5, lr=1e-2) + metrics = eval_fn(model, dev_loader) + assert isinstance(metrics, dict), "evaluate_multilabel_model must return a dictionary." + for key in ["subset_accuracy", "micro_f1", "macro_f1"]: + assert key in metrics, f"Missing multilabel metric: {key}" + assert np.isfinite(metrics[key]), f"Metric {key} must be finite." + print("Tarea 3 public tests passed.") diff --git a/README.md b/README.md new file mode 100644 index 0000000..a0709a0 --- /dev/null +++ b/README.md @@ -0,0 +1,12 @@ +# Procesamiento de Imágenes y Visión por Computadora + +**Nombre:** Alejandro Lembke Barrientos +**Carné:** 12002840 + +Repositorio con los laboratorios de la asignatura. + +## Laboratorios + +| # | Laboratorio | Notebook | +|---|---|---| +| 1 | MLPs, verosimilitud, entrenamiento y evaluación | [Lab 1 - MLP](Lab1/lab1_mlp.ipynb) |