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Set default divergence to histogram based

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This commit is contained in:
Johannes Fischer
2021-08-03 17:18:30 +02:00
parent 5919a4e439
commit cba42c6e4d
1 changed files with 3 additions and 4 deletions
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7
src/metrics.py
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@@ -53,14 +53,14 @@ def metrics(filestr: str, test_dataset, policy):
visualize_distribution(true_actions[:,0], pred_actions[:,0], filestr+'_action_viz') visualize_distribution(true_actions[:,0], pred_actions[:,0], filestr+'_action_viz')
# calculate divergence between acceleration distributions # calculate divergence between acceleration distributions
acceleration_divergence = divergence(pred_actions, true_actions, type='js', n_components=-1) acceleration_divergence = divergence(pred_actions, true_actions, type='js')
info['acceleration_divergence'] = acceleration_divergence info['acceleration_divergence'] = acceleration_divergence
# calculate divergence between velocity distributions # calculate divergence between velocity distributions
sim_velocities = states[:,:,2] sim_velocities = states[:,:,2]
sim_velocities = sim_velocities[~torch.isnan(sim_velocities)].flatten() sim_velocities = sim_velocities[~torch.isnan(sim_velocities)].flatten()
true_velocities = torch.cat(true_velocities, dim=0) true_velocities = torch.cat(true_velocities, dim=0)
velocity_divergence = divergence(sim_velocities, true_velocities, type='js', n_components=-1) velocity_divergence = divergence(sim_velocities, true_velocities, type='js')
info['velocity_divergence'] = velocity_divergence info['velocity_divergence'] = velocity_divergence
return info return info
@@ -96,7 +96,7 @@ def average_velocity(states):
arg_v = nanmean(vehicle_avg_v) arg_v = nanmean(vehicle_avg_v)
return arg_v return arg_v
def divergence(p, q, type='js', n_components=0): def divergence(p, q, type='js', n_components=-1):
""" """
Calculate a divergence between p and q Calculate a divergence between p and q
Args: Args:
@@ -122,7 +122,6 @@ def divergence(p, q, type='js', n_components=0):
m_hist = np.histogram(m, bins=m_bins, density=True, weights=m_weights) m_hist = np.histogram(m, bins=m_bins, density=True, weights=m_weights)
d = .5 * kl_histogram(p, p_hist, m_hist) + .5 * kl_histogram(q, q_hist, m_hist) d = .5 * kl_histogram(p, p_hist, m_hist) + .5 * kl_histogram(q, q_hist, m_hist)
return d return d
elif type == 'kl': elif type == 'kl':
if n_components < 0: if n_components < 0:
# Use histogram binning to discretize sampled distributions # Use histogram binning to discretize sampled distributions