ml/9_pytorch_gs_example.py

import torch.nn.functional as F
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score, f1_score, log_loss
from sklearn.model_selection import train_test_split
from torch import nn, optim

from astrodata.ml.metrics import SklearnMetric
from astrodata.ml.models import PytorchModel
from astrodata.ml.model_selection import GridSearchSelector

if __name__ == "__main__":
    X, y = load_iris(return_X_y=True)

    class IrisNet(nn.Module):
        def __init__(self, input_layers, output_layers):
            super().__init__()
            self.layers = nn.Sequential(
                nn.Linear(input_layers, 16), nn.ReLU(), nn.Linear(16, output_layers)
            )

        def forward(self, x):
            return self.layers(x)

    model = PytorchModel(
        model_class=IrisNet,
        loss_fn=nn.CrossEntropyLoss,
        optimizer=optim.AdamW,
        device="cpu",
    )

    print(model)

    param_grid = {
        "model_params": [{"input_layers": X.shape[1], "output_layers": 3}],
        "optimizer_params": [{"lr": 1e-2}, {"lr": 1e-3}, {"lr": 1e-4}],
        "batch_size": [32, 64],
        "epochs": [5, 10],
    }

    accuracy = SklearnMetric(accuracy_score, greater_is_better=True)
    f1 = SklearnMetric(f1_score, average="micro")
    logloss = SklearnMetric(log_loss)

    metrics = [accuracy, f1, logloss]

    gss = GridSearchSelector(
        model,
        param_grid=param_grid,
        scorer=accuracy,
        val_size=0.1,
        random_state=42,
        metrics=metrics,
    )

    gss.fit(X, y)

    print(gss.get_best_params())
    print(gss.get_best_metrics())