jeddy.py

import torch.nn as nn
import torch.optim as optim
from alias_utils.data import house
from alias_utils.model import RunnerBuilder as Builder
from alias_utils.loss import MSELoss
from alias_utils.metrics import (
    Loss as LossMetric,
    RSquared as RSquaredMetric,
    DesignMatNorm as MNormMetric,
)
from matplotlib import pyplot as plt

def full_data():
    data = house.data()

    wrapper = (
        Builder()
        .name("Model")
        .loss(MSELoss())
        .optimizer(optim.Adam)
        .steps(
            nn.Linear(13, 32),
            nn.Linear(32, 64),
            nn.Linear(64, 32),
            nn.Linear(32, 8),
            nn.Linear(8, 1),
        )
        .with_metric(LossMetric())
        .with_metric(RSquaredMetric())
        .with_metric(MNormMetric())
        .build()
    )

    wrapper.train(data, n_epochs=5000)

    wrapper.plot_two(LossMetric, MNormMetric)
   # wrapper.plot_two(LossMetric, MNormMetric, log=True)
    plt.title('Loss vs Design Matrix Norm on Full Dataset')
    plt.tight_layout()
    plt.show()

def batched_data():
    data = house.data(batch_size = 32)
    wrapper = (
        Builder()
        .name("Model")
        .loss(MSELoss())
        .optimizer(optim.Adam)
        .steps(
            nn.Linear(13, 32),
            nn.Linear(32, 64),
            nn.Linear(64, 32),
            nn.Linear(32, 8),
            nn.Linear(8, 1),
        )
        .with_metric(LossMetric())
        .with_metric(RSquaredMetric())
        .with_metric(MNormMetric())
        .build()
    )

    wrapper.train(data, n_epochs=5000)
    wrapper.plot_two(LossMetric, MNormMetric)
    plt.title('Loss vs Design Matrix Norm on Batched Dataset')
    plt.tight_layout()
    plt.show()



if __name__ == "__main__":
    full_data()

    batched_data()