section 02: I am unable to understand plot_decision_boundry() function

[AS1100K]

import numpy as np

def plot_decision_boundry(model, X, y):
    model.to("cpu")
    X, y = X.to("cpu"), y.to("cpu")

    x_min, x_max = X[:, 0].min(), X[:, 0].max()
    y_min, y_max = X[:, 1].min(), X[:, 1].max()

    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101),
                         np.linspace(y_min, y_max, 101))
    
    # Make features

    X_to_pred_on = torch.from_numpy(np.column_stack((xx.ravel(), yy.ravel()))).float()

    # Make pred
    model.eval()
    with torch.inference_mode():
        y_logits = model(X_to_pred_on)

    # Test for multi-class or binary and adjust logits to prediction labels
    if len(torch.unique(y)) > 2:
        y_pred = torch.softmax(y_logits, dim=1).argmax(dim=1) # mutli-class
    else: 
        y_pred = torch.round(torch.sigmoid(y_logits)) # binary
    
    # Reshape preds and plot
    y_pred = y_pred.reshape(xx.shape).detach().numpy()
    plt.contourf(xx, yy, y_pred, cmap=plt.cm.RdYlBu, alpha=0.7)
    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())

Can Anyone please help me with this code?

[Perian-Yan]

Hi,
the key is to get the coordinates of each grid point (here the number is 101*101), and treat them as a "new dataset" X_pred_on. Then, predict the label (0 or 1) for these grid points. Finally plt.contourf will plot with filled color and also the boundary.

btw, you can always print the intermediate result to see what's going on.

You can refer to
meshgrid
ravel
column_stack
contourf

Hope this helps 😄