Adding First Lab.

Lab1/data/dataset_A_dev.csv

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Lab1/data/dataset_B_dev.csv

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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.")

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) |