def create_tsne_graphs()

in scripts/create_metric_graphs.py [0:0]

• 40 lines of code
• 2 McCabe index (conditional complexity)

def create_tsne_graphs(operation, expt, run_dir, image_dir=args.image_dir):

    saved_pt_dir = f"{run_dir}/activations"
    saved_pts = []

    loss_ts = []
    accuracy_ts = []
    epochs_ts = []
    print(f'glob = {saved_pt_dir + "/activations_*.pt"}')
    files = sorted(glob.glob(saved_pt_dir + "/activations_*.pt"))
    print(f"files = {files}")

    for file in files:
        print(f"Loading {file}")
        saved_pt = torch.load(file)
        saved_pts.append(saved_pt)
        loss_ts.append(saved_pt["val_loss"].mean(dim=-1))
        accuracy_ts.append(saved_pt["val_accuracy"])
        epochs_ts.append(saved_pt["epochs"].squeeze())

    loss_t = torch.cat(loss_ts, dim=0).T.detach()
    accuracy_t = torch.cat(accuracy_ts, dim=0).T.detach()
    epochs_t = torch.cat(epochs_ts, dim=0).detach()
    print(loss_t.shape)
    print(accuracy_t.shape)
    print(epochs_t.shape)
    ######
    a = 0
    num_eqs = len(loss_t)
    b = a + num_eqs

    print("Doing T-SNE..")
    loss_tsne = TSNE(n_components=2, init="pca").fit_transform(loss_t)
    print("...done T-SNE.")

    ncols = 1
    nrows = 1
    fig_width = ncols * 8
    fig_height = nrows * 5
    fig, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(fig_width, fig_height))

    axs.scatter(loss_tsne[:, 0], loss_tsne[:, 1])

    img_file = f"{image_dir}/tsne/{operation}_{expt}.png"
    d = os.path.split(img_file)[0]
    os.makedirs(d, exist_ok=True)
    print(f"Writing {img_file}")
    fig.savefig(img_file)
    plt.close(fig)