38512d92a8682a62e73c5b9e86366888be374532,dragonn/vis/__init__.py,,plot_seq_importance,#Any#Any#Any#Any#Any#Any#Any#Any#,130

Before Change

      xlim: restrict the plotted xrange
      figsize: matplotlib figure size
    
    if show==False:
        plt.ioff()
    else:
        plt.ion() 
    grads=grads.squeeze()
    x=x.squeeze()
    
    seq_len = x.shape[0]

After Change

      xlim: restrict the plotted xrange
      figsize: matplotlib figure size
    
    if axes is None:
        f,axes=plt.subplots(1,dpi=80,figsize=figsize)
        show=True
    else:
        show=False
    grads=grads.squeeze()
    x=x.squeeze()
    
    seq_len = x.shape[0]
    vals_to_plot=grads*x
    if xlim is None:
        xlim = (0, seq_len)
    if ylim is None:
        ylim= (np.amin(vals_to_plot),np.amax(vals_to_plot))
    axes=plot_bases_on_ax(vals_to_plot,axes,show_ticks=True)
    plt.xticks(list(range(xlim[0], xlim[1], 5)))
    plt.xlim(xlim)
    plt.ylim(ylim)
    plt.title(title)
    axes.axvline(x=snp_pos, color="k", linestyle="--")
    if show==True:
        plt.show()
    else:
        return axes


def plot_learning_curve(history):
    train_losses=history.history["loss"]

Instances