def test_compute():
rmse = RootMeanSquaredError()
y_pred = torch.Tensor([[2.0], [-2.0]])
y = torch.zeros(2)
rmse.update((y_pred, y))
assert rmse.compute() == 2.0
rmse.reset()
y_pred = torch.Tensor([[3.0], [-3.0]])
y = torch.zeros(2)
rmse.update((y_pred, y))
assert rmse.compute() == 3.0
def test_compute(n_times, test_data):
rmse = RootMeanSquaredError()
y_pred, y, batch_size = test_data
rmse.reset()
if batch_size > 1:
n_iters = y.shape[0] // batch_size + 1
for i in range(n_iters):
idx = i * batch_size
rmse.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size]))
else:
rmse.update((y_pred, y))
np_y = y.numpy().ravel()
np_y_pred = y_pred.numpy().ravel()
np_res = np.sqrt(np.power((np_y - np_y_pred), 2.0).sum() / np_y.shape[0])
res = rmse.compute()
assert isinstance(res, float)
assert pytest.approx(res) == np_res
class LocalMetrics(ignite.metrics.Metric):
METRICS = (
'rmse',
'pearson',
'per_model_pearson',
)
FIGURES = (
'hist',
)
def __init__(self, column, title=None, metrics=None, figures=None, output_transform=lambda x: x):
self.column = column
self.title = title if title is not None else ''
self.metrics = set(metrics if metrics is not None else LocalMetrics.METRICS)
self.figures = set(figures if figures is not None else LocalMetrics.FIGURES)
self._rmse = RootMeanSquaredError()
self._pearson = PearsonR()
self._per_model_pearson = Mean()
self._hist = ScoreHistogram(title=title)
super(LocalMetrics, self).__init__(output_transform=output_transform)
def reset(self):
self._rmse.reset()
self._pearson.reset()
self._per_model_pearson.reset()
self._hist.reset()
def update(self, batch: DecoyBatch):
# Skip native structures and ignore residues that don't have a ground-truth score
non_native = np.repeat(np.char.not_equal(batch.decoy_name, 'native'),
repeats=batch.num_nodes_by_graph.cpu().numpy())
has_score = torch.isfinite(batch.lddt).cpu().numpy()
valid_scores = np.logical_and(non_native, has_score)
# Used to uniquely identify a (protein, model) pair without using their str names
target_model_id = batch.node_index_by_graph[valid_scores].cpu().numpy()
node_preds = batch.node_features[valid_scores, self.column].detach().cpu().numpy()
node_targets = batch.lddt[valid_scores].detach().cpu().numpy()
# Streaming metrics on local scores (they expect torch tensors, not numpy arrays)
self._rmse.update((torch.from_numpy(node_preds), torch.from_numpy(node_targets)))
self._pearson.update((torch.from_numpy(node_preds), torch.from_numpy(node_targets)))
# Per model metrics: pandas is the easiest way to get a groupby.
grouped = pd.DataFrame({
'target_model': target_model_id,
'preds': node_preds,
'true': node_targets
}).groupby('target_model')
per_model_pearsons = grouped.apply(lambda df: pearson(df['preds'], df['true']))
self._per_model_pearson.update(torch.from_numpy(per_model_pearsons.values))
self._hist.update(node_preds, node_targets)
def compute(self):
metrics = {}
figures = {}
if 'rmse' in self.metrics:
metrics['rmse'] = self._rmse.compute()
if 'pearson' in self.metrics:
metrics['pearson'] = self._pearson.compute()
if 'per_model_pearson' in self.metrics:
metrics['per_model_pearson'] = self._per_model_pearson.compute()
if 'hist' in self.figures:
extra_title = []
if 'pearson' in self.metrics:
extra_title.append(f'$R$ {metrics["pearson"]:.3f}')
if 'per_model_pearson' in self.metrics:
extra_title.append(f'$R_\\mathrm{{model}}$ {metrics["per_model_pearson"]:.3f}')
figures['hist'] = self._hist.compute('\n'.join(extra_title))
return {'metrics': metrics, 'figures': figures}
def completed(self, engine, prefix):
result = self.compute()
for name, metric in result['metrics'].items():
engine.state.metrics[prefix + '/' + name] = metric
for name, fig in result['figures'].items():
engine.state.figures[prefix + '/' + name] = fig
