IA y Prediccion

DataSet

DataSet es el contenedor basico para pares X y Y.

from wsbuilder import DataSet

dataset = DataSet(
    [[1, 2], [2, 3], [3, 4], [4, 5]],
    [[0], [1], [1], [1]],
)
train, test = dataset.split(train_ratio=0.75, shuffle=False)

Metodos utiles:

• sample_count(), feature_count(), target_count()
• copy(), as_xy()
• shuffle(), split(), batches()
• describe_features(), describe_targets()
• standardize(), normalize_min_max()

NeuralNetwork

La red neuronal es completamente Python puro.

from wsbuilder import NeuralNetwork

net = NeuralNetwork(
    seed=7,
    learning_rate=0.3,
    loss="binary_cross_entropy",
    task="classification",
)
net.add_dense(6, input_size=2, activation="tanh")
net.add_dense(1, activation="sigmoid")
history = net.fit(
    [[0, 0], [0, 1], [1, 0], [1, 1]],
    [[0], [1], [1], [0]],
    epochs=5000,
    batch_size=4,
    shuffle=False,
)

Capacidades mas visibles:

• fit para regresion.
• fit_classification para etiquetas simbolicas.
• predict, predict_batch, predict_proba, predict_class.
• evaluate, accuracy, classification_metrics.
• predict_with_metrics para reportes de error y confianza.

DenseLayer

DenseLayer permite definir capas manuales y se usa como bloque interno del NeuralNetwork.

Estadistica y error

Helpers exportados:

• describe_data(values)
• evaluate_errors(expected, predicted, permissible_error=...)

Devuelven DataSummary y ErrorSummary, ambos con uncertainty().

Entrenamiento en background

from wsbuilder import TaskManager, submit_training_task

tasks = TaskManager(max_concurrent=1)
task = submit_training_task(
    tasks,
    net,
    X,
    labels,
    classification=True,
    epochs=1000,
    batch_size=4,
    shuffle=False,
    name="ia-train",
)
history = task.get(timeout=5)

Predictor

Predictor es una utilidad matematica aparte para regresion lineal simple.

from wsbuilder import Predictor

predictor = Predictor()
predictor.fit([[1], [2], [3], [4]], [[2], [4], [6], [8]])
prediction, sigma, lower, upper = predictor.predict([5])

Es util cuando quieres prediccion rapida sin configurar una red neuronal.